From e1f1b5e533b81941af5402cc6fd2ae22445c5f1f Mon Sep 17 00:00:00 2001 From: mlotto Date: Thu, 18 Aug 2022 16:17:29 +0200 Subject: [PATCH 01/29] create develop-Malaria --- vignettes/malaria/FUN_call_data.R | 103 ++++++++++++++++++++++++++++++ vignettes/malaria/call_data.R | 18 ++++++ 2 files changed, 121 insertions(+) create mode 100644 vignettes/malaria/FUN_call_data.R create mode 100644 vignettes/malaria/call_data.R diff --git a/vignettes/malaria/FUN_call_data.R b/vignettes/malaria/FUN_call_data.R new file mode 100644 index 0000000..0f86aeb --- /dev/null +++ b/vignettes/malaria/FUN_call_data.R @@ -0,0 +1,103 @@ +start_obs <- function(var, sys, frequency, year_in, year_fi, mm, lat_min, lat_max, lon_min, lon_max){ + library(startR) + + sdates <- paste0(year_in:year_fi, mm) + + if(sys == 'era5'){ + cds_path <- TRUE # it's the path in /esarchive with updated data from CDS for ERA5; era5land doesn't have this path + }else{ + cds_path <- FALSE + } + + fh <- if(frequency == 'daily'){'f1h'}else{if(var == 'tasmax' | var == 'tasmin'){'f24h'}else{'f1h'}} + # in the case of monthly_mean tas_max(min), + # f24h is the monthly mean of the daily max (min), + # while f1h is the monthly mean of the hourly max (min) + # so in the case of monthly tas_max and tas_min, we want f24h + # in any other case (prlr or daily data) we want f1h + + ref_path <- paste0('/esarchive/recon/ecmwf/', sys, '/', frequency, '_mean/') + repos_obs <- paste0(ref_path, '$var$_', fh, if(cds_path == TRUE){'-r1440x721cds'}, '/$var$_$sdate$.nc') + # ref.path <- paste0(ref_path, paste0(var, '_', fh), if(cds_path == TRUE){'-r1440x721cds'}, '/') + obs <- Start(dat = repos_obs, + var = var, + sdate = sdates, + time = indices(1), + latitude = values(list(lat_min, lat_max)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lon_min, lon_max)), + longitude_reorder = CircularSort(0, 360), + synonims = list(latitude=c('lat','latitude'), + longitude=c('lon','longitude')), + #transform = CDORemapper, + #transform_extra_cells = 2, + #transform_params = list(grid = 'r256x128', + # method = 'conservative', + # crop = c(lon_min,lon_max, lat_min,lat_max)), + #transform_vars = c('latitude', 'longitude'), + return_vars = list(latitude = 'dat', + longitude = 'dat', + time = NULL), + retrieve = TRUE) + lats.ref <- attr(obs, 'Variables')$dat1$latitude + lons.ref <- attr(obs, 'Variables')$dat1$longitude + source_files <- attr(obs, 'Files') + + # dates as list for s2dv_cube object: + dates_for_s2dv <- list(start = array(sdates[1:length(sdates)], dim=c(1,1,length(sdates)), dimnames = list('sdate', 'time')), + end = array(rev(rev(sdates)[1:length(sdates)]), dim=c(1,1,length(sdates)), dimnames = list('sdate', 'time'))) + names(dim(obs)) <- replace(names(dim(obs)), which(names(dim(obs)) == 'latitude'), 'lat') + names(dim(obs)) <- replace(names(dim(obs)), which(names(dim(obs)) == 'longitude'), 'lon') + names(dim(lats.ref)) <- 'lat' + names(dim(lons.ref)) <- 'lon' + library(CSTools) # function s2dv_cube is from CSTools + loaded_data_ref_s2dv <- s2dv_cube(data = obs, lat = lats.ref, lon = lons.ref, Variable = list(varName = var, level = NULL), when = Sys.time(), source_files = source_files, Dates = dates_for_s2dv) + attr(loaded_data_ref_s2dv$lon,"first_lon") <- lon_min + attr(loaded_data_ref_s2dv$lon,"last_lon") <- lon_max + attr(loaded_data_ref_s2dv$lat,"first_lat") <- lat_min + attr(loaded_data_ref_s2dv$lat,"last_lat") <- lat_max + + + return(loaded_data_ref_s2dv) +} + +load_monthly_data_all_variables_and_months <- function(year_in, year_fi, lat_min, lat_max, lon_min, lon_max){ + library(abind) + variables <- c('tasmax', 'tasmin', 'prlr') + months <- c('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12') + for(var in variables){ + for(mm in months){ + data <- start_obs( + var = var, # tas, tasmax, tasmin, prlr, ... + sys = 'era5land', # era5, era5land + frequency = 'monthly', # monthly, daily + year_in = year_in, + year_fi = year_fi, + mm = mm, + lat_min = lat_min, + lat_max = lat_max, + lon_min = lon_min, + lon_max = lon_max) + + if(mm == months[1]){ + loaded_data <- data + dimnames <- names(dim(loaded_data$data)) + loaded_data$data <- abind(data$data, along = 4) # dim 4 is 'time' + names(dim(loaded_data$data)) <- dimnames + } else { + loaded_data$data <- abind(loaded_data$data, data$data, along = 4) + names(dim(loaded_data$data)) <- dimnames + } + } + if(var == 'tasmax'){ + loaded_tasmax <- loaded_data + } + if(var == 'tasmin'){ + loaded_tasmin <- loaded_data + } + if(var == 'prlr'){ + loaded_prlr <- loaded_data + } + } + return(list(tasmax_obs = loaded_tasmax, tasmin_obs = loaded_tasmin, prlr_obs = loaded_prlr)) +} diff --git a/vignettes/malaria/call_data.R b/vignettes/malaria/call_data.R new file mode 100644 index 0000000..fc406a1 --- /dev/null +++ b/vignettes/malaria/call_data.R @@ -0,0 +1,18 @@ +# Learning how to use StartR +setwd("/home/Earth/mlotto/lcd_projects/csindicators/vignettes/malaria") + +source("call_data.R") + +# If I want to call the monthly temperature data, I would have to: +data <- Start(dat = repos, + var = 'tas', + sdate = '195001', + # Retrieve brings the data into the R session, FALSE by default + retrieve=TRUE, + time = indices(1), + latitude = values(c(1, 15)), + longitude = values(c(1, 40)), + synonims = list(latitude=c('lat','latitude'), + longitude=c('lon','longitude'))) + + -- GitLab From 96e7bcf3a0fd4d6a55dfa62e51b3ea0bc347c528 Mon Sep 17 00:00:00 2001 From: mlotto Date: Wed, 24 Aug 2022 10:10:05 +0200 Subject: [PATCH 02/29] compute climate indicator --- .gitignore | 1 + vignettes/malaria/FUN_call_data.R | 103 -------------------------- vignettes/malaria/call_data.R | 102 ++++++++++++++++++++++---- vignettes/malaria/ind_computation.R | 109 ++++++++++++++++++++++++++++ vignettes/malaria/malaria.Rproj | 13 ++++ vignettes/malaria/plot_map.R | 13 ++++ 6 files changed, 223 insertions(+), 118 deletions(-) delete mode 100644 vignettes/malaria/FUN_call_data.R create mode 100644 vignettes/malaria/ind_computation.R create mode 100644 vignettes/malaria/malaria.Rproj create mode 100644 vignettes/malaria/plot_map.R diff --git a/.gitignore b/.gitignore index 23a5603..022a306 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,4 @@ Rplots.pdf *.RData !data/*.RData .\.nfs* +.Rproj.user diff --git a/vignettes/malaria/FUN_call_data.R b/vignettes/malaria/FUN_call_data.R deleted file mode 100644 index 0f86aeb..0000000 --- a/vignettes/malaria/FUN_call_data.R +++ /dev/null @@ -1,103 +0,0 @@ -start_obs <- function(var, sys, frequency, year_in, year_fi, mm, lat_min, lat_max, lon_min, lon_max){ - library(startR) - - sdates <- paste0(year_in:year_fi, mm) - - if(sys == 'era5'){ - cds_path <- TRUE # it's the path in /esarchive with updated data from CDS for ERA5; era5land doesn't have this path - }else{ - cds_path <- FALSE - } - - fh <- if(frequency == 'daily'){'f1h'}else{if(var == 'tasmax' | var == 'tasmin'){'f24h'}else{'f1h'}} - # in the case of monthly_mean tas_max(min), - # f24h is the monthly mean of the daily max (min), - # while f1h is the monthly mean of the hourly max (min) - # so in the case of monthly tas_max and tas_min, we want f24h - # in any other case (prlr or daily data) we want f1h - - ref_path <- paste0('/esarchive/recon/ecmwf/', sys, '/', frequency, '_mean/') - repos_obs <- paste0(ref_path, '$var$_', fh, if(cds_path == TRUE){'-r1440x721cds'}, '/$var$_$sdate$.nc') - # ref.path <- paste0(ref_path, paste0(var, '_', fh), if(cds_path == TRUE){'-r1440x721cds'}, '/') - obs <- Start(dat = repos_obs, - var = var, - sdate = sdates, - time = indices(1), - latitude = values(list(lat_min, lat_max)), - latitude_reorder = Sort(decreasing = TRUE), - longitude = values(list(lon_min, lon_max)), - longitude_reorder = CircularSort(0, 360), - synonims = list(latitude=c('lat','latitude'), - longitude=c('lon','longitude')), - #transform = CDORemapper, - #transform_extra_cells = 2, - #transform_params = list(grid = 'r256x128', - # method = 'conservative', - # crop = c(lon_min,lon_max, lat_min,lat_max)), - #transform_vars = c('latitude', 'longitude'), - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = NULL), - retrieve = TRUE) - lats.ref <- attr(obs, 'Variables')$dat1$latitude - lons.ref <- attr(obs, 'Variables')$dat1$longitude - source_files <- attr(obs, 'Files') - - # dates as list for s2dv_cube object: - dates_for_s2dv <- list(start = array(sdates[1:length(sdates)], dim=c(1,1,length(sdates)), dimnames = list('sdate', 'time')), - end = array(rev(rev(sdates)[1:length(sdates)]), dim=c(1,1,length(sdates)), dimnames = list('sdate', 'time'))) - names(dim(obs)) <- replace(names(dim(obs)), which(names(dim(obs)) == 'latitude'), 'lat') - names(dim(obs)) <- replace(names(dim(obs)), which(names(dim(obs)) == 'longitude'), 'lon') - names(dim(lats.ref)) <- 'lat' - names(dim(lons.ref)) <- 'lon' - library(CSTools) # function s2dv_cube is from CSTools - loaded_data_ref_s2dv <- s2dv_cube(data = obs, lat = lats.ref, lon = lons.ref, Variable = list(varName = var, level = NULL), when = Sys.time(), source_files = source_files, Dates = dates_for_s2dv) - attr(loaded_data_ref_s2dv$lon,"first_lon") <- lon_min - attr(loaded_data_ref_s2dv$lon,"last_lon") <- lon_max - attr(loaded_data_ref_s2dv$lat,"first_lat") <- lat_min - attr(loaded_data_ref_s2dv$lat,"last_lat") <- lat_max - - - return(loaded_data_ref_s2dv) -} - -load_monthly_data_all_variables_and_months <- function(year_in, year_fi, lat_min, lat_max, lon_min, lon_max){ - library(abind) - variables <- c('tasmax', 'tasmin', 'prlr') - months <- c('01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12') - for(var in variables){ - for(mm in months){ - data <- start_obs( - var = var, # tas, tasmax, tasmin, prlr, ... - sys = 'era5land', # era5, era5land - frequency = 'monthly', # monthly, daily - year_in = year_in, - year_fi = year_fi, - mm = mm, - lat_min = lat_min, - lat_max = lat_max, - lon_min = lon_min, - lon_max = lon_max) - - if(mm == months[1]){ - loaded_data <- data - dimnames <- names(dim(loaded_data$data)) - loaded_data$data <- abind(data$data, along = 4) # dim 4 is 'time' - names(dim(loaded_data$data)) <- dimnames - } else { - loaded_data$data <- abind(loaded_data$data, data$data, along = 4) - names(dim(loaded_data$data)) <- dimnames - } - } - if(var == 'tasmax'){ - loaded_tasmax <- loaded_data - } - if(var == 'tasmin'){ - loaded_tasmin <- loaded_data - } - if(var == 'prlr'){ - loaded_prlr <- loaded_data - } - } - return(list(tasmax_obs = loaded_tasmax, tasmin_obs = loaded_tasmin, prlr_obs = loaded_prlr)) -} diff --git a/vignettes/malaria/call_data.R b/vignettes/malaria/call_data.R index fc406a1..57d219b 100644 --- a/vignettes/malaria/call_data.R +++ b/vignettes/malaria/call_data.R @@ -1,18 +1,90 @@ # Learning how to use StartR -setwd("/home/Earth/mlotto/lcd_projects/csindicators/vignettes/malaria") - -source("call_data.R") - -# If I want to call the monthly temperature data, I would have to: -data <- Start(dat = repos, - var = 'tas', - sdate = '195001', - # Retrieve brings the data into the R session, FALSE by default - retrieve=TRUE, - time = indices(1), - latitude = values(c(1, 15)), - longitude = values(c(1, 40)), - synonims = list(latitude=c('lat','latitude'), - longitude=c('lon','longitude'))) +#setwd("/home/Earth/mlotto/lcd_projects/csindicators/vignettes/malaria") +# source("call_data.R") +source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") + +# load several months and bind them to the same R object: +# Dimensions: +# - var= number of variables (in general just one) +# - sdate= number of years +# - time= number of months +# - lat and lon= coordinates + +# example of use +# +path_ERA5tas_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc') + + year_in <- 2010 + year_fi <- 2011 + month_in <- 1 + month_fi <- 12 + + sdates <- paste0(year_in:year_fi, '0101') + + loaded_obs <- CST_Load_s2dv(var = 'tas', + exp = NULL, + obs = list(path_ERA5tas_CDS), + sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'monthly', + leadtimemin = month_in, leadtimemax = month_fi, + output = "lonlat") + + + loaded_obs2 <- CST_Load_s2dv(var = 'prlr', + exp = NULL, + obs = list(path_ERA5tas_CDS), + sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'monthly', + leadtimemin = month_in, leadtimemax = month_fi, + output = "lonlat") + + tas3 <- tas2 <- tas1 <- tas <- loaded_obs + pr3 <- pr2 <- pr1 <- pr <- loaded_obs2 + + tas1$data <- tas$data - 273.15 + pr1$data <- pr$data * 30410 + + tas3$data <- ifelse(tas1$data >= 4 & pr1$data >= 0.0012, 1, 0) + + test1$data <- loaded_obs$data - 273.15 + test2$data <- ifelse(test1$data >= 5, NA, test1$data) + test2$data <- ifelse(test1$data >= 4 & test1$data <=5, 1, 0) + test2$data <- ifelse(test1$data >= 4 & test1$data <=5, 1, 0) + + + + + summary(test1$data) + + + + test2$data <- test$data+test$data + test3$data <- test$data*2 + + # change the unit of temperature from °C to K + + tas_exp$data <- tas_exp$data - 273.15 + tas_obs$data <- tas_obs$data - 273.15 + + + +dim(loaded_obs$data) +#dataset member sdate ftime lat lon +# 1 1 4 12 8 8 + + + + +year_in <- 2013 +year_fi <- 2014 +lat_min <- 41 +lat_max <- 41.75 +lon_min <- 352.25 +lon_max <- 353 +data <- load_monthly_data_all_variables_and_months(year_in, year_fi, lat_min, lat_max, lon_min, lon_max) diff --git a/vignettes/malaria/ind_computation.R b/vignettes/malaria/ind_computation.R new file mode 100644 index 0000000..4b703be --- /dev/null +++ b/vignettes/malaria/ind_computation.R @@ -0,0 +1,109 @@ +########### +# 1. Extract temperature data for a period of 10 years and for a specific region of the Iberian Peninsula +# 2. Convert temperature from Kelvin to Degrees +# 3. Extract dew point temperature for the same period and region +# 4. Convert temperature from Kelvin to Degrees +# 5. Compute relative humidity +# 6. Extract precipitation for the same period and region +# 7. Convert m to mm +# 8. Create the binary indicator +# 9. Use totaltimeexceedingthreshold to create summary value per year +######### + +source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") +source("plot_map.R") + +path_ERA5tas_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc') + +year_in <- 2020 +year_fi <- 2021 +month_in <- 1 +month_fi <- 12 + +sdates <- paste0(year_in:year_fi, '0101') + +### TEMPERATURE (TAS) +# 1. Extract sample temperature +tas_obs <- CST_Load_s2dv(var = 'tas', + exp = NULL, + obs = list(path_ERA5tas_CDS), + sdates = sdates, + lonmax = 355, lonmin = 350, + latmax = 45, latmin = 43, + storefreq = 'monthly', + leadtimemin = month_in, + leadtimemax = month_fi, + output = "lonlat") + +# 2. Convert temperature from Kelvin to Degrees +tas_obs$data <- tas_obs$data-273.15 + +### DEW POINT TEMPERATURE (TDPS) +# 3. Extract sample dew point temperature +tdps_obs <- CST_Load_s2dv(var = 'tdps', + exp = NULL, + obs = list(path_ERA5tas_CDS), + sdates = sdates, + lonmax = 355, lonmin = 350, + latmax = 45, latmin = 43, + storefreq = 'monthly', + leadtimemin = month_in, + leadtimemax = month_fi, + output = "lonlat") + + +# 4. Convert from Kelvin to Degrees +tdps_obs$data <- tdps_obs$data-273.15 + +# 5. Compute rel humidity +rel_hum <- tdps_obs +rel_hum$data <- 100*(exp((17.625*tdps_obs$data)/(243.04+tdps_obs$data)) / exp((17.625*tas_obs$data) / (243.04+tas_obs$data))) + +#### PRECIPITATION (PRLR) +# 6. Extract sample precipitation +pr_obs <- CST_Load_s2dv(var = 'prlr', + exp = NULL, + obs = list(path_ERA5tas_CDS), + sdates = sdates, + lonmax = 355, lonmin = 350, + latmax = 45, latmin = 43, + storefreq = 'monthly', + leadtimemin = month_in, + leadtimemax = month_fi, + output = "lonlat") + +# 7. Convert m/month to mm/moth +# Precipitation is downloaded and transformed as a flux of meters per second per +# month, to transform it to mm/month it is necessary to multiply it by: +# - total number of seconds in one hour: 3600 +# - total number of hours in a day: 24 +# - average number of days in a month: 30.41 +# - change from meter to mm: 1000 +pr_obs$data <- pr_obs$data*3600*24*30.41*1000 + +#### INDICATOR +# 8. Create the binary indicator +# i) Create individual suitability per variable for checking purposes +t1 <- r1 <- p1 <- pr_obs +t1$data <- ifelse(tas_obs$data >= 14.5 & tas_obs$data <= 33,1,0) +# tas_obs$data[1,1,1,6,,][15:19, 15:20] +# t1$data[1,1,1,6,,][15:19, 15:20] + +r1$data <- ifelse(rel_hum$data >= 60,1,0) +# rel_hum$data[1,1,1,6,,][15:19, 15:20] +# r1$data[1,1,1,6,,][15:19, 15:20] + +p1$data <- ifelse(pr_obs$data >= 80,1,0) +# pr_obs$data[1,1,1,6,,][15:19, 15:20] +# p1$data[1,1,1,6,,][15:19, 15:20] + +# ii) Create climate suitability +suit_ind <- pr_obs +suit_ind$data <- ifelse(tas_obs$data >= 14.5 & tas_obs$data <= 33 & rel_hum$data >= 60 & pr_obs$data >= 80,1,0) +# suit_ind$data[1,1,1,6,,][15:19, 15:20] + +# 9. Use CSIndicators::TotalTimeExceedingThreshold() to compute indicator +test <- CSIndicators::CST_TotalTimeExceedingThreshold(suit_ind, threshold=0.5)#, time_dim="sdate") + +dim(test$data) +test$data[1,1,1,,][15:19, 15:20] \ No newline at end of file diff --git a/vignettes/malaria/malaria.Rproj b/vignettes/malaria/malaria.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/vignettes/malaria/malaria.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/vignettes/malaria/plot_map.R b/vignettes/malaria/plot_map.R new file mode 100644 index 0000000..9ebeaaa --- /dev/null +++ b/vignettes/malaria/plot_map.R @@ -0,0 +1,13 @@ +# Use this configuration to create a plot + +# There is bug in the PlotEquiMap function from CSTools and you have to set +# the following before using it +source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotEquiMap.R') +.FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs +.KnownLonNames <- s2dv:::.KnownLonNames +.KnownLatNames <- s2dv:::.KnownLatNames +ColorBar <- s2dv::ColorBar +clim.palette <- s2dv::clim.palette +.IsColor<- s2dv:::.IsColor + +# END \ No newline at end of file -- GitLab From b56cc2e78a6cdd197304f8dc4d666ebfb5d41ae7 Mon Sep 17 00:00:00 2001 From: mlotto Date: Fri, 26 Aug 2022 16:46:28 +0200 Subject: [PATCH 03/29] start vignette --- vignettes/malaria/MalariaIndicator.Rmd | 190 ++++++++ vignettes/malaria/MalariaIndicator.html | 495 +++++++++++++++++++++ vignettes/malaria/ind_computation.R | 27 +- vignettes/malaria/make_figs.R | 3 + vignettes/malaria/rest.R | 557 ++++++++++++++++++++++++ 5 files changed, 1271 insertions(+), 1 deletion(-) create mode 100644 vignettes/malaria/MalariaIndicator.Rmd create mode 100644 vignettes/malaria/MalariaIndicator.html create mode 100644 vignettes/malaria/make_figs.R create mode 100644 vignettes/malaria/rest.R diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd new file mode 100644 index 0000000..5a9dec0 --- /dev/null +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -0,0 +1,190 @@ +--- +title: "Malaria Suitability Indicator" +author: "Earth Sciences department, Barcelona Supercomputing Center (BSC)" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteEngine{knitr::knitr} + %\VignetteIndexEntry{Agricultural Indicators} + %\usepackage[utf8]{inputenc} +--- + + + +```{r echo=FALSE} +knitr::opts_chunk$set(warnings=FALSE, message=FALSE) +``` + +Climate Suitability for Malaria Transmission +----------------------------- + + +## Introduction + +Climate sensitive infectious diseases are a subject of major concern for public +health agencies around the world in the context of global change. Malaria is an +infectious disease caused by parasites of the genus _Plasmodium_ and transmitted +by mosquitoes of the genus _Anopheles_. Although Europe has been free of endemic +malaria circulation since the early 1970s as a result of improving socio-economic +conditions, improving climatic conditions pose a risk of malaria re emergence in +the continent. + +In the context of the Lancet Countdown in Europe collaboration, the Climate Suitability +for Malaria indicator was created for tracking the climatic conditions for transmission +of this disease in the continent between 1950 and 2021. This indicator is based +on the climatic requirements from Plasmodium vivax, former parasite endemic to Europe. + +In this vignette, we propose to show the computation of the Climate Suitability +for Malaria indicator using the **TotalTimeExceedingThreshold -** function from +the CSIndicators package. This function is able to operate with multidimensional +arrays and s2dv_cube objects. In this example, we will make use of the +**CST_TotalTimeExceedingThreshold** function, which was developed for the latter +(For further information about the different types of objects visit the section +about [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) +from CSTools package ). + +## Load libraries + +In addition to the functiosn from the **CSIndicators** package, we will make use +of functions from the **CSTools** package. + +```{r} +library(CSIndicators) +library(CSTools) +``` + +Temporary links to functions +```{r} +source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") +``` + + +## 1. Data extraction +### 1.1 Area of interest +In this vignette, the malaria indicator between 2010 and 2020 will be computed +for the Iberian Peninsula. + +* Which countries? +* What coordinates? + +[Wikipedia image](https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg) + +### 1.2 Data extraction +To compute the malaria indicator, we will use reanalysis data from the ERA5-Land +repository. These data are produced by the Copernicus service and extracted and +curated by the Earth Sciences department from the Barcelona Supercomputing Center. +The function `CST_Load` from the `CSTools` package allows the user to access the +processed data and to work with it using function from the `CSIndicators` package. + +In order to extract data using `CST_Load`, there are some requirements to be met. +First, the path to the files is introduced using *whitecards* delimited by dollar +signs, which are useful for iterating over variables. Second, the temporal extent +and resolution of the output is inputted with `sdates`, which takes a vector of all +necessary dates. The spatial extent is insputed within `lonmax`, `lonmin`, `latmax` +and `latmin.` + +```{r} +# Path to file locations using whitecards +path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc') + +# Temporal extent and resolution: +# We will work with all months from 2010 to 2020 +year_in <- 2020 # initial year +year_fi <- 2021 # last year +month_in <- 1 # first month +month_fi <- 12 # last month + +sdates <- paste0(year_in:year_fi, '0101') + +# Extract data +vars <- c('tas', 'tdps', 'prlr') + +out <- NULL +for(var in vars){ + out[[var]] <- CST_Load_s2dv(var = var, + exp = NULL, # We only require observved data + obs = list(path_ERA5_CDS), + sdates = sdates, + lonmax = 355, lonmin = 350, + latmax = 45, latmin = 43, + storefreq = 'monthly', + leadtimemin = month_in, + leadtimemax = month_fi, + output = "lonlat") +} +``` + +This function creates `s2dv_cube` objects, defined as XXXX. For further information +about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). + +As an example, we will show the dimensions of near surface temperature + +```{r} +dim(out$tas$data) +``` +Near surface temperature is made up of 10 years with 12 months per year distributed +over a grid of XXxXX. + + +## 2. Data transformation +### 2.1 Near surface temperature and near surface dew point temperature +The first step before working with temperature is transforming their unit from +Kelvin (K) to Degrees Celsius (C). + +```{r} +summary(out$tas$data) +``` + + +```{r} +for(var in c('tas', 'tdps')){ + out[[var]]$data <- out[[var]]$data - 273.15 +} + +summary(out$tas$data) +``` + +### 2.2 Precipitation +```{r} +summary(out$prlr$data) +``` + + +```{r} +out$prlr$data <- out$prlr$data*3600*24*30.41*1000 + +summary(out$prlr$data) +``` + +### 2.3 Relative humidity +```{r} +out$rh <- out$tas + +out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / + exp((17.625*out$tas$data) / (243.04+out$tas$data))) + +summary(out$rh$data) +``` + +### 2.4 Land use +Add chunk + +## 3. Compute indicator + + + +## 4. Visualization +Add chunk + +## 5. Summary statistics +Add chunk + +## 6. Conclusions +Add chunk + +## References +Add text \ No newline at end of file diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html new file mode 100644 index 0000000..c8e3023 --- /dev/null +++ b/vignettes/malaria/MalariaIndicator.html @@ -0,0 +1,495 @@ + + + + + + + + + + + + + + + + +Malaria Suitability Indicator + + + + + + + + + + + + + + + + + + + + + + + + + +

Malaria Suitability Indicator

+

Earth Sciences department, Barcelona Supercomputing Center (BSC)

+

2022-08-26

+ + + + +
+

Climate Suitability for Malaria Transmission

+
+
+

Introduction

+

Climate sensitive infectious diseases are a subject of major concern for public health agencies around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by mosquitoes of the genus Anopheles. Although Europe has been free of endemic malaria circulation since the early 1970s as a result of improving socio-economic conditions, improving climatic conditions pose a risk of malaria re emergence in the continent.

+

In the context of the Lancet Countdown in Europe collaboration, the Climate Suitability for Malaria indicator was created for tracking the climatic conditions for transmission of this disease in the continent between 1950 and 2021. This indicator is based on the climatic requirements from Plasmodium vivax, former parasite endemic to Europe.

+

In this vignette, we propose to show the computation of the Climate Suitability for Malaria indicator using the TotalTimeExceedingThreshold - function from the CSIndicators package. This function is able to operate with multidimensional arrays and s2dv_cube objects. In this example, we will make use of the CST_TotalTimeExceedingThreshold function, which was developed for the latter (For further information about the different types of objects visit the section about Data retrieval and storage from CSTools package ).

+
+
+

Load libraries

+

In addition to the functiosn from the CSIndicators package, we will make use of functions from the CSTools package.

+
library(CSIndicators)
+library(CSTools)
+

Temporary links to functions

+
source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R")
+
+
+

1. Data extraction

+
+

1.1 Area of interest

+

In this vignette, the malaria indicator between 2010 and 2020 will be computed for the Iberian Peninsula.

+
    +
  • Which countries?
    • +
  • What coordinates?
    • +
+

Wikipedia image

+
+
+

1.2 Data extraction

+

To compute the malaria indicator, we will use reanalysis data from the ERA5-Land repository. These data are produced by the Copernicus service and extracted and curated by the Earth Sciences department from the Barcelona Supercomputing Center. The function CST_Load from the CSTools package allows the user to access the processed data and to work with it using function from the CSIndicators package.

+

In order to extract data using CST_Load, there are some requirements to be met. First, the path to the files is introduced using whitecards delimited by dollar signs, which are useful for iterating over variables. Second, the temporal extent and resolution of the output is inputted with sdates, which takes a vector of all necessary dates. The spatial extent is insputed within lonmax, lonmin, latmax and latmin.

+
# Path to file locations using whitecards
+path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
+
+# Temporal extent and resolution:
+# We will work with all months from 2010 to 2020
+year_in <- 2020 # initial year
+year_fi <- 2021 # last year
+month_in <- 1 # first month
+month_fi <- 12 # last month
+
+sdates <- paste0(year_in:year_fi, '0101')
+
+# Extract data
+vars <- c('tas', 'tdps', 'prlr')
+
+out <- NULL
+for(var in vars){
+  out[[var]] <- CST_Load_s2dv(var = var,
+                           exp = NULL, # We only require observved data
+                           obs = list(path_ERA5_CDS),
+                           sdates = sdates,
+                           lonmax = 355, lonmin = 350,
+                           latmax = 45, latmin = 43,
+                           storefreq = 'monthly',
+                           leadtimemin = month_in, 
+                           leadtimemax = month_fi,
+                           output = "lonlat")  
+}
+

This function creates s2dv_cube objects, defined as XXXX. For further information about these objects visit Data retrieval and storage.

+

As an example, we will show the dimensions of near surface temperature

+
dim(out$tas$data)
+
## dataset  member   sdate   ftime     lat     lon 
+##       1       1       2      12      21      51
+

Near surface temperature is made up of 10 years with 12 months per year distributed over a grid of XXxXX.

+
+
+
+

2. Data transformation

+
+

2.1 Near surface temperature and near surface dew point temperature

+

The first step before working with temperature is transforming their unit from Kelvin (K) to Degrees Celsius (C).

+
summary(out$tas$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##   272.5   281.8   284.7   284.9   288.7   292.9   19680
+
for(var in c('tas', 'tdps')){
+  out[[var]]$data <- out[[var]]$data - 273.15
+}
+
+summary(out$tas$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##   -0.67    8.61   11.56   11.77   15.56   19.78   19680
+
+
+

2.2 Precipitation

+
summary(out$prlr$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##       0       0       0       0       0       0   19680
+
out$prlr$data <- out$prlr$data*3600*24*30.41*1000
+
+summary(out$prlr$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##    2.22   61.84   99.72  107.85  142.38  395.53   19680
+
+
+

2.3 Relative humidity

+
out$rh <- out$tas
+
+out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
+                      exp((17.625*out$tas$data) / (243.04+out$tas$data)))
+
+summary(out$rh$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##   67.55   79.30   81.90   81.88   83.97  116.08   19680
+
+
+

2.4 Land use

+

Add chunk

+
+
+
+

3. Compute indicator

+
+
+

4. Visualization

+

Add chunk

+
+
+

5. Summary statistics

+

Add chunk

+
+
+

6. Conclusions

+

Add chunk

+
+
+

References

+

Add text

+
+ + + + + + + + + + + diff --git a/vignettes/malaria/ind_computation.R b/vignettes/malaria/ind_computation.R index 4b703be..8a7658f 100644 --- a/vignettes/malaria/ind_computation.R +++ b/vignettes/malaria/ind_computation.R @@ -106,4 +106,29 @@ suit_ind$data <- ifelse(tas_obs$data >= 14.5 & tas_obs$data <= 33 & rel_hum$data test <- CSIndicators::CST_TotalTimeExceedingThreshold(suit_ind, threshold=0.5)#, time_dim="sdate") dim(test$data) -test$data[1,1,1,,][15:19, 15:20] \ No newline at end of file +test$data[1,1,2,,][15:19, 15:20] + + +############## +# Test raster file +library(raster) + + +path.c <- "/esarchive/recon/ecmwf/era5land/monthly_mean/tas_f1h/tas_202108.nc" +templ <- raster(path.c) + +e <- raster::extent(350, 355, 43, 45) +templ <- crop(templ, e) +templ1 <- projectRaster(templ, crs = crs(test), res = res(test)) + +path <- "/esarchive/recon/ecmwf/clc/original_files/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif" +test <- raster(path) +r <- crop(test, extent(templ1)) + +test1 <- as.matrix(r) +test2 <- s2dv_cube(test1) + +test1 <- s2dv_cube(array(r, dim=c(dat=1, sdate=1, ftime=1))) + + + diff --git a/vignettes/malaria/make_figs.R b/vignettes/malaria/make_figs.R new file mode 100644 index 0000000..f8ef703 --- /dev/null +++ b/vignettes/malaria/make_figs.R @@ -0,0 +1,3 @@ +# Creaing figures for CSIndicators vignette about the malaria indicator + +source("plot_map.R") \ No newline at end of file diff --git a/vignettes/malaria/rest.R b/vignettes/malaria/rest.R new file mode 100644 index 0000000..13d8979 --- /dev/null +++ b/vignettes/malaria/rest.R @@ -0,0 +1,557 @@ +vignette: > + %\VignetteEngine{knitr::knitr} +%\VignetteIndexEntry{Agricultural Indicators} +%\usepackage[utf8]{inputenc} + +[Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) + +### 1. Load libraries and obtain data + +We will work with the following library + +``` +library(CSIndicators) +library(CSTools) +library(s2dv) +``` + +Climate information used in the + +To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). + +The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in Section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) from CSTools package. + +The spatial domain covers part of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. + +With `grid` set to **r1440x721**, the SEAS5 forecast would be interpolated to the 0.25-degree ERA5 grid by using the **bicubic** method given in `method`. + + +``` +S5path_prlr <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$_mean/$VAR_NAME$_s0-24h/$VAR_NAME$_$START_DATE$.nc') + +path_ERA5prlr_CDS <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') + +sdates <- paste0(2013:2016, '04', '01') + +c(prlr_exp, prlr_obs) %<-% CST_Load(var = 'prlr', + exp = list(S5path_prlr), + obs = list(path_ERA5prlr_CDS), + sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, + nmember = 3, output = "lonlat", + grid = "r1440x721", method = 'bicubic') +``` + + +The output contains data and metadata for the experiment and the observations. The elements `prlr_exp$data` and `prlr_obs$data` have dimensions: + + + ``` +dim(prlr_exp$data) +#dataset member sdate ftime lat lon +# 1 3 4 214 4 4 +dim(prlr_obs$data) +#dataset member sdate ftime lat lon +# 1 1 4 214 4 4 +``` + + +To compute **SprR** of forecast and observation, we can run: + + + ``` +SprR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 4), end = list(21, 6)) +SprR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 4), end = list(21, 6)) +``` + +The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). + +As mentioned, these parameters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. + +The dimensions of SprR forecasts and observations are: + + ``` +dim(SprR_exp$data) +#dataset member sdate lat lon +# 1 3 4 4 4 +dim(SprR_obs$data) +#dataset member sdate lat lon +# 1 1 4 4 4 +``` + +The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: + + ``` +SprR_exp$data[1,1,,1,1] * 86400 * 1000 +#[1] 93.23205 230.41904 194.01412 226.52614 +``` + +Dry springs will delay vegetative growth and reduce vigour and leaf area total surface. Fungal disease pressure will be lower and therefore there will be less need for protective and / or curative treatments, translating as less costs. Wet springs will promote higher vigour, increase the risk of fungal disease and disrupt vineyard operations as it may prevent machinery from getting in the vineyard due to mud. They are usually associated with higher costs. + +On the other hand, another moisture-related indicators, **HarvestR**, can be computed by using `PeriodAccumulation` as well, with the defined period as the following lines. + +``` +HarvestR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 8), end = list(21, 10)) +HarvestR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 8), end = list(21, 10)) +``` + +The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid point in mm are: + + ``` +HarvestR_exp$data[1,1,,1,1] * 86400 * 1000 +#[1] 52.30026 42.88068 156.87961 32.18579 +``` + +To compute the 2013-2016 ensemble-mean bias of forecast HarvestR, run + +``` +fcst <- drop(HarvestR_exp$data) * 86400 * 1000 +obs <- drop(HarvestR_obs$data) * 86400 * 1000 + +Bias <- MeanDims((fcst - InsertDim(obs, 1, dim(fcst)['member'])), 'member') +``` + +To plot the map of ensemble-mean bias of HarvestR forecast, run + +``` +cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', + '#92c5de', '#4393c3', '#2166ac') + +PlotEquiMap(Bias[1,,], lon = prlr_obs$lon, lat = prlr_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'mm', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], + margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], + brks = seq(-60, 60, 20), colNA = 'white', + toptitle = 'Ensemble-mean bias of HarvestR in 2013', + bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +``` +You will see the following maps of HarvestR bias in 2013. + + + + In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). + + +### 2. PeriodMean + +For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. GST is defined as the average of daily average temperatures between April 1st to October 31st in the Northern Hemisphere. It provides information onto which are the best suited varieties for a given site or, inversely, which are the best places to grow a specific variety. For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. Many grapevine varieties across the world have been characterized in function of their GST optimum. + +Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running + +``` +S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') +ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') + +c(tas_exp, tas_obs) %<-% CST_Load(var = 'tas', exp = list(S5path), obs = list(ERA5path), + sdates = sdates, lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, + storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, + nmember = 3, output = "lonlat", + grid = "r1440x721", method = 'bicubic') +``` + +The output contains observations `tas_dv$obs$data` and forecast `tas_dv$exp$data`, and their dimensions and summaries are like + +``` +dim(tas_obs$data) +#dataset member sdate ftime lat lon +# 1 1 4 214 4 4 + +dim(tas_exp$data) +#dataset member sdate ftime lat lon +# 1 3 4 214 4 4 + +summary(tas_obs$data - 273.15) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 3.63 13.97 17.25 17.29 20.75 30.21 + +summary(tas_exp$data - 273.15) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.54 11.65 16.56 16.50 21.25 31.41 + +``` +To compute the GST for both observation and forecast, run the following lines + +``` +# change the unit of temperature from °C to K + +tas_exp$data <- tas_exp$data - 273.15 +tas_obs$data <- tas_obs$data - 273.15 + +# compute GST + +GST_exp <- CST_PeriodMean(tas_exp, start = list(1, 4), end = list(31, 10)) +GST_obs <- CST_PeriodMean(tas_obs, start = list(1, 4), end = list(31, 10)) + +``` + +Since the period considered for GST is the entire period for starting month of April, in this case the `start` and `end` parameters could be ignored. + +The summaries and dimensions of the output are as follows: + + ``` +summary(GST_exp$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 14.23 15.78 16.50 16.50 17.17 18.70 + +summary(GST_obs$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 15.34 16.77 17.22 17.29 18.00 18.75 + +dim(GST_exp$data) +#dataset member sdate lat lon +# 1 3 4 4 4 + +dim(GST_obs$data) +#dataset member sdate lat lon +# 1 1 4 4 4 +``` + +Here, we plot the 2013-2016 mean climatology of ERA5 GST by running + +``` +# compute ERA5 GST climatology +GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') + +cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') +PlotEquiMap(GST_Clim, lon = tas_obs$lon, lat = tas_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = '°C', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], + margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], + brks = seq(16, 18.5, 0.5), colNA = 'white', bar_label_scale = 1.5, + toptitle = '2013-2016 mean ERA5 GST', + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +``` + +The ERA5 GST climatology is shown as below. + + + + ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. + + +### 3. TotalTimeExceedingThreshold + +For the function `TotalTimeExceedingThreshold`, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. 35°C is the average established threshold for photosynthesis to occur in the grapevine. Above this temperature, the plant closes its stomata. If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. The higher the index, the lower will be the berry quality and aptitude to produce quality grapes. + +SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. There are three indicators sharing the similar definition as SU35: SU36, SU40 and Spr32. Their definition are listed as follows. + +1. **SU36**: Total count of days when daily maximum temperatures exceed 36°C between June 21st and September 21st +2. **SU40**: Total count of days when daily maximum temperatures exceed 40°C between June 21st and September 21st +3. **Spr32**: Total count of days when daily maximum temperatures exceed 32°C between April 21st and June 21st + +These indicators can be computed as well by using the function `TotalTimeExceedingThreshold` with different thresholds and periods indicated. + +Here, we take SU35 as example, therefore the daily temperature maximum of the entire 7-month forecast period is needed for the computation of this indicator. + +Load SEAS5 and ERA5 daily temperature maximum by running + +``` +S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') +ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') + +c(tasmax_exp, tasmax_obs) %<-% CST_Load(var = 'tasmax', exp = list(S5path), + obs = list(ERA5path), sdates = sdates, + lonmax = 353, lonmin = 352.25, + latmax = 41.75, latmin = 41, storefreq = 'daily', + leadtimemin = 1, leadtimemax = 214, nmember = 3, + output = "lonlat", grid = "r1440x721", + method = 'bicubic', nprocs = 1) +``` + +Check the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). + +``` +tasmax_exp$data <- tasmax_exp$data - 273.15 +tasmax_obs$data <- tasmax_obs$data - 273.15 +``` + +Computing SU35 for forecast and observation by running + +``` +threshold <- 35 +SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_exp, threshold = threshold, + start = list(1, 4), end = list(31, 10)) +SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_obs, threshold = threshold, + start = list(1, 4), end = list(31, 10)) +``` + +The summaries of SU35 forecasts and observations are given below. + +``` +summary(SU35_exp$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.00 2.00 5.00 7.12 12.00 26.00 + +summary(SU35_obs$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.000 0.000 1.000 2.609 5.000 10.000 + +``` +As shown in the summaries, SEAS5 SU35 forecasts are overestimated by 5 days in terms of mean value. + +Therefore, `CST_BiasCorrection` is used to bias adjust the SU35 forecasts. + +``` +res <- CST_BiasCorrection(obs = SU35_obs, exp = SU35_exp) +SU35_exp_BC <- drop(res$data) +summary(SU35_exp_BC) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# -1.419 0.000 1.613 2.831 4.756 17.768 +``` + +Since there are negative values after bias adjustment, all negative data is converted to zero. + +``` +SU35_exp_BC[SU35_exp_BC < 0] <- 0 +summary(SU35_exp_BC) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.000 0.000 1.613 2.943 4.756 17.768 +``` + +Plot the bias-adjusted SU35 forecast in 2016 by running + +``` +SU35_obs_Y2016 <- drop(SU35_obs$data)[4, , ] +SU35_exp_Y2016 <- MeanDims(drop(SU35_exp$data)[, 4, , ], 'member') +SU35_exp_BC_Y2016 <- MeanDims(SU35_exp_BC[, 4, , ], 'member') +cols <- c("#fee5d9", "#fcae91", "#fb6a4a", "#de2d26","#a50f15") + +toptitle <- 'ERA5 SU35 forecast in 2016' +PlotEquiMap(SU35_obs_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + +toptitle <- 'SU35 forecast in 2016' +PlotEquiMap(SU35_exp_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + +toptitle <- 'Bias-adjusted SU35 forecast in 2016' +PlotEquiMap(SU35_exp_BC_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +``` +You can see the figure as below. + + + + + + + As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. + +Beside of the original definition of SU35, here two supplementary functions in the package `CSIndicators` are demonstrated by computing its another definition with the percentile adjustment. + +--- + 1. **AbsToProbs -**: to transform ensemble forecast into probabilities by using the Cumulative Distribution Function +2. **QThreshold -**: to transform an absolute threshold into probabilities. +--- + + The above two supplementary functions are required to compute SU35 with the percentile adjustment. The function `AbsToProbs` would be applied to forecast and the `QThreshold` would be used to convert the observations to its percentile based on the given threshold. + +The revised definition of SU35 is to reduce the potential influence induced by the fixed threshold of temperature defined for the index, instead of using the absolute value, the percentile corresponding to 35°C for observation is compared to the percentile corresponding to the predicted daily maximum temperature before being considered as a ‘heat stress’ day. + +As mentioned, the forecast is translated to its percentile by using the function `ABsToProbs` by running + +``` +exp_percentile <- AbsToProbs(tasmax_exp$data) +S5txP <- aperm(drop(exp_percentile), c(2, 1, 3, 4, 5)) +``` + +After that, based on 35 of threshold, the percentile corresponding to each observational value can be calculated as follows. + +``` +obs_percentile <- QThreshold(tasmax_obs$data, threshold = 35) +obs_percentile <- drop(obs_percentile) +``` + +After translating both forecasts and observations into probabilities, the comparison can then be done by running + +``` +SU35_exp_Percentile <- TotalTimeExceedingThreshold(S5txP, threshold = obs_percentile, time_dim = 'ftime') +``` + +Compute the same ensemble-mean SU35 **with percentile adjustment** in 2016 by running + +``` +SU35_exp_per_Y2016 <- MeanDims(SU35_exp_Percentile[, 4, , ], 'member') +``` + +Plot the same map for comparison + +``` +toptitle <- 'SU35 forecast with percentile adjustment in 2016' +PlotEquiMap(SU35_exp_per_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'day', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), + toptitle = toptitle, + colNA = cols[1], bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) + +``` + + + + + As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. + +The performance of comparison of skills between two definitions requires further analysis such as the application of more skill metrics. + +### 4. AccumulationExceedingThreshold + +The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree Days). + +The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. Here, the tas (daily average temperature) used above (in Section 2. PeriodMean) is loaded again (Please re-use the section of loading tas in Section 2). As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. + +*Note: The data is in degrees Celsiusi at this point* + + ``` +GDD_exp <- CST_AccumulationExceedingThreshold(tas_exp, threshold = 10, diff = TRUE) +GDD_obs <- CST_AccumulationExceedingThreshold(tas_obs, threshold = 10, diff = TRUE) +``` + +The summaries of GDD are + +``` +summary(GDD_exp$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 1021 1331 1480 1469 1596 1873 + +summary(GDD_obs$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 1195 1483 1569 1583 1730 1874 +``` + +To compute the correlation coefficient for the period from 2013-2016, run the following lines + +``` +# reorder the dimension +fcst <- Reorder(drop(GDD_exp$data), c(4, 3, 2, 1)) +obs <- Reorder(drop(GDD_obs$data), c(3, 2, 1)) + +EnsCorr <- veriApply('EnsCorr', fcst = fcst, obs = obs, ensdim = 4, tdim = 3) +GDD_Corr <- Reorder(EnsCorr, c(2, 1)) +``` + +To plot the map of correlation coefficient of GDD for the 2013-2016 period. + +``` +cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") +toptitle <- '2013-2016 correlation coefficient of GDD' +PlotEquiMap(GDD_Corr, lon = tas_obs$lon, lat = tas_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'correlation', + title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, + margin_scale = c(1, 1, 1, 1), cols = cols, brks = seq(0.5, 1, 0.1), + toptitle = toptitle, bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +``` + +The map of correlation coefficient for the 2013-2016 period is shown as below. + + + + The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. + +### 5. TotalSpellTimeExceedingThreshold + +One of the critical agricultural indicators related to dry spell is the **Warm Spell Duration Index (WSDI)**, which is defined as the total count of days with at least 6 consecutive days when the daily maximum temperature exceeds its 90th percentile in the seven months into the future. + +The maximum temperature data used in Section 3. Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. + +``` +tx_p <- CST_Threshold(tasmax_obs, threshold = 0.9) +``` + +The output will be the 90th percentile of each day of each grid point derived by using all the years in the data.See the dimension and summary as below. + +``` +dim(tx_p$data) +#dataset ftime lat lon +# 1 214 4 4 + +summary(tx_p$data) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 13.83 22.08 26.08 26.22 30.72 36.72 +``` + +With the prepared threshold (90th percentile), the WSDI can be computed by running + +``` +WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_exp, threshold = tx_p, spell = 6) +WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_obs, threshold = tx_p, spell = 6) +``` + +After checking the summaries, compute the Fair Ranked Probability Skill Score (FRPSS) of WSDI by running the following lines + +``` +# Reorder the data +fcst <- Reorder(drop(WSDI_exp$data), c(4, 3, 2, 1)) +obs <- Reorder(drop(WSDI_obs$data), c(3, 2, 1)) + +# summaries of WSDI +summary(fcst) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 0.00 13.00 28.00 31.22 46.00 82.00 + +summary(obs) +# Min. 1st Qu. Median Mean 3rd Qu. Max. +# 9.00 19.00 22.50 23.16 26.00 33.00 + +# compute FRPSS +f <- veriApply('FairRpss', fcst = fcst, obs = obs, ensdim = 4, tdim = 3, prob = 1:2/3)$skillscore +WSDI_FRPSS <- Reorder(f, c(2,1)) +``` + +Plot the map of WSDI FRPSS for the period from 2013-2016 + +``` +cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") +toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' + +PlotEquiMap(WSDI_FRPSS, lon = tasmax_obs$lon, lat = tasmax_obs$lat, + intylat = 1, intxlon = 1, width = 6, height = 6, + filled.continents = FALSE, units = 'FRPSS', title_scale = .8, + axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), + cols = cols[1:3], col_inf = 'white', col_sup = cols[4], + brks = seq(0, 0.9, 0.3), toptitle = toptitle, bar_label_scale = 1.5, + bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) +``` + +The FRPSS map for 2013-2016 SEAS WSDI is shown as below. + + + + As seen in the map, the FRPSS in the eastern part of Douro Valley falls in 0.6-0.9, which are good enough to be useful when compared to observational climatology. + + +In addition to the grape/wine sector focused here, the MEDGOLD project also works on the other two sectors: olive/olive oil and durum wheat/pasta. Furthermore, the climate services are also provided at the longer term (up to 30 years) by other project partners. + +Click on [MEDGOLD](https://www.med-gold.eu/climate-services/) for more information. + + + -- GitLab From 22c84b9bc4dd74205f3a32e3896acaa78e1ec81c Mon Sep 17 00:00:00 2001 From: mlotto Date: Sun, 28 Aug 2022 16:33:30 +0200 Subject: [PATCH 04/29] add text to vignette --- vignettes/malaria/MalariaIndicator.Rmd | 230 +++++++--- vignettes/malaria/MalariaIndicator.html | 236 ++++++---- vignettes/malaria/plot_map.R | 13 - vignettes/malaria/rest.R | 557 ------------------------ 4 files changed, 318 insertions(+), 718 deletions(-) delete mode 100644 vignettes/malaria/plot_map.R delete mode 100644 vignettes/malaria/rest.R diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd index 5a9dec0..9e43f52 100644 --- a/vignettes/malaria/MalariaIndicator.Rmd +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -26,66 +26,86 @@ Climate Suitability for Malaria Transmission ## Introduction Climate sensitive infectious diseases are a subject of major concern for public -health agencies around the world in the context of global change. Malaria is an -infectious disease caused by parasites of the genus _Plasmodium_ and transmitted -by mosquitoes of the genus _Anopheles_. Although Europe has been free of endemic -malaria circulation since the early 1970s as a result of improving socio-economic -conditions, improving climatic conditions pose a risk of malaria re emergence in -the continent. - -In the context of the Lancet Countdown in Europe collaboration, the Climate Suitability -for Malaria indicator was created for tracking the climatic conditions for transmission -of this disease in the continent between 1950 and 2021. This indicator is based -on the climatic requirements from Plasmodium vivax, former parasite endemic to Europe. - -In this vignette, we propose to show the computation of the Climate Suitability -for Malaria indicator using the **TotalTimeExceedingThreshold -** function from -the CSIndicators package. This function is able to operate with multidimensional -arrays and s2dv_cube objects. In this example, we will make use of the -**CST_TotalTimeExceedingThreshold** function, which was developed for the latter +health around the world in the context of global change. Malaria is an infectious +disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ +mosquitoes. Eradication efforts managed to achieve complete elimination of endemic +malaria in Europe since 1974, although sporadic transmission events are frequently +reported in travelers coming from other endemic areas. Studies evaluating the factors +that keep malaria out of the continent found that high socio-economic conditions +and increased life expectancy are key (ref). On the other hand, climatic conditions are +becoming more suitable for malaria transmission (ref). + +The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose +of using a variety of indicators to monitor trends in the impact of climate on the +dynamic of different health processes (ref). As part of this initiative, the Climate +Suitability for Malaria Transmission indicator was adapted from the Global Lancet +Countdown Collaboration for tracking the number of months suitable of malaria +transmission per year in the continent between 1950 and 2021. This indicator is a +threshold based model that overlaps the climatic and environmental requirements of +_Anopheles_ mosquitoes and _Plasmodium vivax_, which was the parasite of endemic +circulation in the continent until the mid 1970s. + +Although the `CSIndicators` was initially developed for providing a set of tools +useful in the context of agricultural applications, it is also possible to expand +its applications to the computation of other type of indicators. In this vignette, +the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold` function. The output will be an multidimensional +array containing the number of months per year suitable for malaria transmission. + + + (For further information about the different types of objects visit the section about [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) from CSTools package ). ## Load libraries -In addition to the functiosn from the **CSIndicators** package, we will make use -of functions from the **CSTools** package. +In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will +be used for data extraction and visualization. -```{r} -library(CSIndicators) +```{r eval=FALSE} library(CSTools) +library(CSIndicators) +library(s2dv) ``` -Temporary links to functions -```{r} + +```{r echo=FALSE} +#Temporary links to functions +library(CSIndicators) source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") ``` -## 1. Data extraction +## 1. Data ### 1.1 Area of interest -In this vignette, the malaria indicator between 2010 and 2020 will be computed -for the Iberian Peninsula. +The number of months suitable for malaria transmission between 2010 and 2020 will +be computed for the Iberian Peninsula (40º14´24´´N 4º14´21´´W). This area comprises +the territories of Portugal, Spain, France, Andorra and Gibraltar. -* Which countries? -* What coordinates? - -[Wikipedia image](https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg) +```{r echo=FALSE} +knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg") +``` ### 1.2 Data extraction -To compute the malaria indicator, we will use reanalysis data from the ERA5-Land -repository. These data are produced by the Copernicus service and extracted and -curated by the Earth Sciences department from the Barcelona Supercomputing Center. +Malaria indicator is computed with climate and environmental data. Climate variables +are extracted from the ERA5-Land reanalysis dataset (ref), which span between 1950 +and present day, at a 9km resolution. Necessary climate variables are 2 meter +temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation + (`prlr`). Environmental information is extracted from the CORINE Land Cover inventory, +which classifies land into 44 classes at a 100 m resolution. These data are produced +by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** +from the **Barcelona Supercomputing Center**. + The function `CST_Load` from the `CSTools` package allows the user to access the -processed data and to work with it using function from the `CSIndicators` package. +processed data and delivers the data in the form of an `s2dv_cube` object. These +multidimensional objects store data and metadata in several elements. For further +information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). -In order to extract data using `CST_Load`, there are some requirements to be met. -First, the path to the files is introduced using *whitecards* delimited by dollar -signs, which are useful for iterating over variables. Second, the temporal extent -and resolution of the output is inputted with `sdates`, which takes a vector of all -necessary dates. The spatial extent is insputed within `lonmax`, `lonmin`, `latmax` -and `latmin.` +`CST_Load` has its requirements for extracting data. First, the path to the files +is introduced using *whitecards* delimited by dollar signs. Second, the temporal +extent and resolution of the output con be provided with `sdates`, which takes a +vector of all dates of interest. The spatial extent is inputted within `lonmax`, +`lonmin`, `latmax` and `latmin.` ```{r} # Path to file locations using whitecards @@ -106,7 +126,7 @@ vars <- c('tas', 'tdps', 'prlr') out <- NULL for(var in vars){ out[[var]] <- CST_Load_s2dv(var = var, - exp = NULL, # We only require observved data + exp = NULL, # We only require observed data obs = list(path_ERA5_CDS), sdates = sdates, lonmax = 355, lonmin = 350, @@ -116,30 +136,40 @@ for(var in vars){ leadtimemax = month_fi, output = "lonlat") } -``` -This function creates `s2dv_cube` objects, defined as XXXX. For further information -about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). - -As an example, we will show the dimensions of near surface temperature +# library(zeallot) +# c(tas, tdps, prlr) %<-% CST_Load_s2dv(var = c('tas', 'tdps', 'prlr'), +# exp = NULL, # We only require observed data +# obs = list(path_ERA5_CDS), +# sdates = sdates, +# lonmax = 355, lonmin = 350, +# latmax = 45, latmin = 43, +# storefreq = 'monthly', +# leadtimemin = month_in, +# leadtimemax = month_fi, +# output = "lonlat") +``` +The call above creates a list with the three datasets. Each `s2dv_cube` object has +the following dimensions: ```{r} dim(out$tas$data) ``` -Near surface temperature is made up of 10 years with 12 months per year distributed -over a grid of XXxXX. - - -## 2. Data transformation -### 2.1 Near surface temperature and near surface dew point temperature -The first step before working with temperature is transforming their unit from -Kelvin (K) to Degrees Celsius (C). - +And it is possible to have a look at the data: ```{r} +# A summary of the entire dataset summary(out$tas$data) ``` +```{r} +# Sneak peek of a sample of the temperature data in Jan 2010 +out$tas$data[1,1,1,1,,][15:20, 15:20] +``` +## 2. Data transformation +### 2.1 Temperature and dew point temperature +This variables will be used to compute relative humidity, which requires them to +be in Degrees Celsius (C): ```{r} for(var in c('tas', 'tdps')){ out[[var]]$data <- out[[var]]$data - 273.15 @@ -148,36 +178,102 @@ for(var in c('tas', 'tdps')){ summary(out$tas$data) ``` -### 2.2 Precipitation -```{r} -summary(out$prlr$data) -``` - +### 2.3 Relative humidity +After transforming temperature ($T$) and dew point temperature ($T_d$) units from Kelvin (K) to +Degrees Celsius (C), it is possible to compute the relative humidity using +August-Roche-Magnus equation (ref): -```{r} -out$prlr$data <- out$prlr$data*3600*24*30.41*1000 +$$ +RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} +$$ +Where a and b are the coefficients 17.625 and 243.04, respectively. -summary(out$prlr$data) -``` - -### 2.3 Relative humidity ```{r} +# Create a template with the same dimensions as the datasets out$rh <- out$tas +# Compute relative humidity (RH) out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / exp((17.625*out$tas$data) / (243.04+out$tas$data))) summary(out$rh$data) ``` +### 2.2 Precipitation +Precipitation is extracted from the hourly reanalaysis dataset and processes as +monthly meters per second. For the computation of the malaria indicator, it is +necessary to transform it into accumulated mm per month: +```{r} +# To compute the accumulated precipitation per month it is necessary to multiply by: +# - total number of seconds in one hour: 3600 +# - total number of hours in a day: 24 +# - average number of days in a month: 30.41 +# - change factor from meter to mm: 1000 +out$prlr$data <- out$prlr$data*3600*24*30.41*1000 + +summary(out$prlr$data) +``` + ### 2.4 Land use Add chunk ## 3. Compute indicator +```{r} +# Create a template with the same dimensions as the datasets +malariaSuit <- out$tas + +# Determine suitability +malariaSuit$data <- ifelse(out$tas$data >= 14.5 & + out$tas$data <= 33 & + out$rh$data >= 60 & + out$prlr$data >= 80, 1, 0) + +# Compute number of number of months per year that are suitable +# (i.e. suitability = 1) +malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(malariaSuit, + threshold=0.5) + +# Sneak peek of the data +malariaInd$data[1,1,1,,][15:20, 15:20] +``` +## 4. Visualization +### 4.1. Map +Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to +have a visual inspection of the number of suitable months in January 2010. +```{r echo=FALSE} +# Until the dependency with s2dverification in CSTools is updated, it is necessary +# to make these adjustments +source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotEquiMap.R') +.FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs +.KnownLonNames <- s2dv:::.KnownLonNames +.KnownLatNames <- s2dv:::.KnownLatNames +ColorBar <- s2dv::ColorBar +clim.palette <- s2dv::clim.palette +.IsColor<- s2dv:::.IsColor +``` -## 4. Visualization +```{r fig.width=8, fig.height=4} +PlotLayout(PlotEquiMap, c('lat', 'lon'), + var=malariaInd$data[1,1,,,], + nrow=1, ncol=2, + lon=malariaInd$lon, + lat=malariaInd$lat, + filled.continents=FALSE, + brks=seq(0, 12, by=1), + toptitle="Number of months per year suitable for malaria in the Iberian Peninsula", + titles=c("2010", "2011"), + title_scale=0.5, + coast_width=2, + filled.oceans=TRUE, + country.borders=TRUE, + intylat=1, + intxlon=1) + +``` + +### 4.2 Time series Add chunk ## 5. Summary statistics diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html index c8e3023..f627289 100644 --- a/vignettes/malaria/MalariaIndicator.html +++ b/vignettes/malaria/MalariaIndicator.html @@ -12,7 +12,7 @@ - + Malaria Suitability Indicator @@ -336,7 +336,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }

Malaria Suitability Indicator

Earth Sciences department, Barcelona Supercomputing Center (BSC)

-

2022-08-26

+

2022-08-28

@@ -350,107 +350,135 @@ text-align: justify

Introduction

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Climate sensitive infectious diseases are a subject of major concern for public health agencies around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by mosquitoes of the genus Anopheles. Although Europe has been free of endemic malaria circulation since the early 1970s as a result of improving socio-economic conditions, improving climatic conditions pose a risk of malaria re emergence in the continent.

-

In the context of the Lancet Countdown in Europe collaboration, the Climate Suitability for Malaria indicator was created for tracking the climatic conditions for transmission of this disease in the continent between 1950 and 2021. This indicator is based on the climatic requirements from Plasmodium vivax, former parasite endemic to Europe.

-

In this vignette, we propose to show the computation of the Climate Suitability for Malaria indicator using the TotalTimeExceedingThreshold - function from the CSIndicators package. This function is able to operate with multidimensional arrays and s2dv_cube objects. In this example, we will make use of the CST_TotalTimeExceedingThreshold function, which was developed for the latter (For further information about the different types of objects visit the section about Data retrieval and storage from CSTools package ).

+

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes. Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (ref). On the other hand, climatic conditions are becoming more suitable for malaria transmission (ref).

+

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes (ref). As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021. This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasite of endemic circulation in the continent until the mid 1970s.

+

Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

+

(For further information about the different types of objects visit the section about Data retrieval and storage from CSTools package ).

Load libraries

-

In addition to the functiosn from the CSIndicators package, we will make use of functions from the CSTools package.

-
library(CSIndicators)
-library(CSTools)
-

Temporary links to functions

-
source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R")
+

In addition to the CSIndicators package, the CSTools and s2dv packages will be used for data extraction and visualization.

+
library(CSTools)
+library(CSIndicators)
+library(s2dv)
-
-

1. Data extraction

+
+

1. Data

1.1 Area of interest

-

In this vignette, the malaria indicator between 2010 and 2020 will be computed for the Iberian Peninsula.

-
    -
  • Which countries?
    • -
  • What coordinates?
    • -
-

Wikipedia image

+

The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula (40º14´24´´N 4º14´21´´W). This area comprises the territories of Portugal, Spain, France, Andorra and Gibraltar.

+
-
+

1.2 Data extraction

-

To compute the malaria indicator, we will use reanalysis data from the ERA5-Land repository. These data are produced by the Copernicus service and extracted and curated by the Earth Sciences department from the Barcelona Supercomputing Center. The function CST_Load from the CSTools package allows the user to access the processed data and to work with it using function from the CSIndicators package.

-

In order to extract data using CST_Load, there are some requirements to be met. First, the path to the files is introduced using whitecards delimited by dollar signs, which are useful for iterating over variables. Second, the temporal extent and resolution of the output is inputted with sdates, which takes a vector of all necessary dates. The spatial extent is insputed within lonmax, lonmin, latmax and latmin.

-
# Path to file locations using whitecards
-path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
-
-# Temporal extent and resolution:
-# We will work with all months from 2010 to 2020
-year_in <- 2020 # initial year
-year_fi <- 2021 # last year
-month_in <- 1 # first month
-month_fi <- 12 # last month
-
-sdates <- paste0(year_in:year_fi, '0101')
-
-# Extract data
-vars <- c('tas', 'tdps', 'prlr')
-
-out <- NULL
-for(var in vars){
-  out[[var]] <- CST_Load_s2dv(var = var,
-                           exp = NULL, # We only require observved data
-                           obs = list(path_ERA5_CDS),
-                           sdates = sdates,
-                           lonmax = 355, lonmin = 350,
-                           latmax = 45, latmin = 43,
-                           storefreq = 'monthly',
-                           leadtimemin = month_in, 
-                           leadtimemax = month_fi,
-                           output = "lonlat")  
-}
-

This function creates s2dv_cube objects, defined as XXXX. For further information about these objects visit Data retrieval and storage.

-

As an example, we will show the dimensions of near surface temperature

-
dim(out$tas$data)
+

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (ref), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory, which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

+

The function CST_Load from the CSTools package allows the user to access the processed data and delivers the data in the form of an s2dv_cube object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit Data retrieval and storage.

+

CST_Load has its requirements for extracting data. First, the path to the files is introduced using whitecards delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with sdates, which takes a vector of all dates of interest. The spatial extent is inputted within lonmax, lonmin, latmax and latmin.

+
# Path to file locations using whitecards
+path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
+
+# Temporal extent and resolution:
+# We will work with all months from 2010 to 2020
+year_in <- 2020 # initial year
+year_fi <- 2021 # last year
+month_in <- 1 # first month
+month_fi <- 12 # last month
+
+sdates <- paste0(year_in:year_fi, '0101')
+
+# Extract data
+vars <- c('tas', 'tdps', 'prlr')
+
+out <- NULL
+for(var in vars){
+  out[[var]] <- CST_Load_s2dv(var = var,
+                           exp = NULL, # We only require observed data
+                           obs = list(path_ERA5_CDS),
+                           sdates = sdates,
+                           lonmax = 355, lonmin = 350,
+                           latmax = 45, latmin = 43,
+                           storefreq = 'monthly',
+                           leadtimemin = month_in, 
+                           leadtimemax = month_fi,
+                           output = "lonlat")  
+}
+
+# library(zeallot)
+# c(tas, tdps, prlr) %<-% CST_Load_s2dv(var = c('tas', 'tdps', 'prlr'),
+#                                       exp = NULL, # We only require observed data
+#                                       obs = list(path_ERA5_CDS),
+#                                       sdates = sdates,
+#                                       lonmax = 355, lonmin = 350,
+#                                       latmax = 45, latmin = 43,
+#                                       storefreq = 'monthly',
+#                                       leadtimemin = month_in, 
+#                                       leadtimemax = month_fi,
+#                                       output = "lonlat")
+

The call above creates a list with the three datasets. Each s2dv_cube object has the following dimensions:

+
dim(out$tas$data)
## dataset  member   sdate   ftime     lat     lon 
 ##       1       1       2      12      21      51
-

Near surface temperature is made up of 10 years with 12 months per year distributed over a grid of XXxXX.

+

And it is possible to have a look at the data:

+
# A summary of the entire dataset
+summary(out$tas$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##   272.5   281.8   284.7   284.9   288.7   292.9   19680
+
# Sneak peek of a sample of the temperature data in Jan 2010
+out$tas$data[1,1,1,1,,][15:20, 15:20]
+
##        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]
+## [1,]     NA     NA     NA     NA 283.70 283.39
+## [2,]     NA     NA     NA     NA 283.36 282.70
+## [3,]     NA     NA     NA 283.27 283.00 282.03
+## [4,] 282.94 282.79 282.59 282.57 282.37 281.64
+## [5,] 281.89 281.58 281.53 281.56 281.39 280.84
+## [6,] 281.18 280.96 280.88 280.82 280.56 280.11

2. Data transformation

-
-

2.1 Near surface temperature and near surface dew point temperature

-

The first step before working with temperature is transforming their unit from Kelvin (K) to Degrees Celsius (C).

-
summary(out$tas$data)
-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##   272.5   281.8   284.7   284.9   288.7   292.9   19680
-
for(var in c('tas', 'tdps')){
-  out[[var]]$data <- out[[var]]$data - 273.15
-}
-
-summary(out$tas$data)
+
+

2.1 Temperature and dew point temperature

+

This variables will be used to compute relative humidity, which requires them to be in Degrees Celsius (C):

+
for(var in c('tas', 'tdps')){
+  out[[var]]$data <- out[[var]]$data - 273.15
+}
+
+summary(out$tas$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 ##   -0.67    8.61   11.56   11.77   15.56   19.78   19680
-
-

2.2 Precipitation

-
summary(out$prlr$data)
-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##       0       0       0       0       0       0   19680
-
out$prlr$data <- out$prlr$data*3600*24*30.41*1000
-
-summary(out$prlr$data)
-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##    2.22   61.84   99.72  107.85  142.38  395.53   19680
-

2.3 Relative humidity

-
out$rh <- out$tas
-
-out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
-                      exp((17.625*out$tas$data) / (243.04+out$tas$data)))
-
-summary(out$rh$data)
+

After transforming temperature (\(T\)) and dew point temperature (\(T_d\)) units from Kelvin (K) to Degrees Celsius (C), it is possible to compute the relative humidity using August-Roche-Magnus equation (ref):

+

\[ +RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} +\] Where a and b are the coefficients 17.625 and 243.04, respectively.

+
# Create a template with the same dimensions as the datasets
+out$rh <- out$tas
+
+# Compute relative humidity (RH)
+out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
+                      exp((17.625*out$tas$data) / (243.04+out$tas$data)))
+
+summary(out$rh$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 ##   67.55   79.30   81.90   81.88   83.97  116.08   19680
+
+

2.2 Precipitation

+

Precipitation is extracted from the hourly reanalaysis dataset and processes as monthly meters per second. For the computation of the malaria indicator, it is necessary to transform it into accumulated mm per month:

+
# To compute the accumulated precipitation per month it is necessary to multiply by:
+# - total number of seconds in one hour: 3600
+# - total number of hours in a day: 24
+# - average number of days in a month: 30.41
+# - change factor from meter to mm: 1000
+out$prlr$data <- out$prlr$data*3600*24*30.41*1000
+
+summary(out$prlr$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##    2.22   61.84   99.72  107.85  142.38  395.53   19680
+

2.4 Land use

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@@ -458,11 +486,57 @@ text-align: justify

3. Compute indicator

+
# Create a template with the same dimensions as the datasets
+malariaSuit <- out$tas
+
+# Determine suitability
+malariaSuit$data <- ifelse(out$tas$data >= 14.5 & 
+                             out$tas$data <= 33 & 
+                             out$rh$data >= 60 & 
+                             out$prlr$data >= 80, 1, 0)
+
+# Compute number of number of months per year that are suitable
+# (i.e. suitability = 1)
+malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(malariaSuit, 
+                                                            threshold=0.5)
+
+# Sneak peek of the data
+malariaInd$data[1,1,1,,][15:20, 15:20]
+
##      [,1] [,2] [,3] [,4] [,5] [,6]
+## [1,]   NA   NA   NA   NA    2    2
+## [2,]   NA   NA   NA   NA    2    2
+## [3,]   NA   NA   NA    2    1    2
+## [4,]    1    1    1    1    1    2
+## [5,]    1    1    1    1    1    1
+## [6,]    0    1    1    1    1    1

4. Visualization

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+

4.1. Map

+

Using the function PlotEquiMap() from the s2dv package, it is possible to have a visual inspection of the number of suitable months in January 2010.

+
PlotLayout(PlotEquiMap, c('lat', 'lon'),
+           var=malariaInd$data[1,1,,,],
+           nrow=1, ncol=2,
+           lon=malariaInd$lon,
+           lat=malariaInd$lat,
+           filled.continents=FALSE,
+           brks=seq(0, 12, by=1),
+           toptitle="Number of months per year suitable for malaria in the Iberian Peninsula",
+           titles=c("2010", "2011"),
+           title_scale=0.5,
+           coast_width=2,
+           filled.oceans=TRUE,
+           country.borders=TRUE,
+           intylat=1,
+           intxlon=1)
+

+
+
+

4.2 Time series

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5. Summary statistics

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diff --git a/vignettes/malaria/plot_map.R b/vignettes/malaria/plot_map.R deleted file mode 100644 index 9ebeaaa..0000000 --- a/vignettes/malaria/plot_map.R +++ /dev/null @@ -1,13 +0,0 @@ -# Use this configuration to create a plot - -# There is bug in the PlotEquiMap function from CSTools and you have to set -# the following before using it -source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotEquiMap.R') -.FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs -.KnownLonNames <- s2dv:::.KnownLonNames -.KnownLatNames <- s2dv:::.KnownLatNames -ColorBar <- s2dv::ColorBar -clim.palette <- s2dv::clim.palette -.IsColor<- s2dv:::.IsColor - -# END \ No newline at end of file diff --git a/vignettes/malaria/rest.R b/vignettes/malaria/rest.R deleted file mode 100644 index 13d8979..0000000 --- a/vignettes/malaria/rest.R +++ /dev/null @@ -1,557 +0,0 @@ -vignette: > - %\VignetteEngine{knitr::knitr} -%\VignetteIndexEntry{Agricultural Indicators} -%\usepackage[utf8]{inputenc} - -[Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) - -### 1. Load libraries and obtain data - -We will work with the following library - -``` -library(CSIndicators) -library(CSTools) -library(s2dv) -``` - -Climate information used in the - -To obtain the precipitation forecast and observation, we load the daily precipitation (**prlr** given in `var`) data sets of ECMWF SEAS5 seasonal forecast and ERA5 reanalysis for the four starting dates 20130401-20160401 (provided in `sdates`) with the entire 7-month forecast time, April-October (214 days in total given in parameter `leadtimemax`). - -The pathways of SEAS5 and ERA5 are given in the lists with some **whitecards (inside two dollar signs)** used to replace the variable name and iterative items such as year and month. See details of requirements in Section 4 in vignette [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) from CSTools package. - -The spatial domain covers part of Douro Valley of Northern Portugal lon=[352.25, 353], lat=[41, 41.75]. These four values are provided in `lonmin`, `lonmax`, `latmin` and `latmax`. - -With `grid` set to **r1440x721**, the SEAS5 forecast would be interpolated to the 0.25-degree ERA5 grid by using the **bicubic** method given in `method`. - - -``` -S5path_prlr <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$_mean/$VAR_NAME$_s0-24h/$VAR_NAME$_$START_DATE$.nc') - -path_ERA5prlr_CDS <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') - -sdates <- paste0(2013:2016, '04', '01') - -c(prlr_exp, prlr_obs) %<-% CST_Load(var = 'prlr', - exp = list(S5path_prlr), - obs = list(path_ERA5prlr_CDS), - sdates = sdates, - lonmax = 353, lonmin = 352.25, - latmax = 41.75, latmin = 41, - storefreq = 'daily', - leadtimemin = 1, leadtimemax = 214, - nmember = 3, output = "lonlat", - grid = "r1440x721", method = 'bicubic') -``` - - -The output contains data and metadata for the experiment and the observations. The elements `prlr_exp$data` and `prlr_obs$data` have dimensions: - - - ``` -dim(prlr_exp$data) -#dataset member sdate ftime lat lon -# 1 3 4 214 4 4 -dim(prlr_obs$data) -#dataset member sdate ftime lat lon -# 1 1 4 214 4 4 -``` - - -To compute **SprR** of forecast and observation, we can run: - - - ``` -SprR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 4), end = list(21, 6)) -SprR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 4), end = list(21, 6)) -``` - -The `start` and `end` are the initial and final dates and the day must be given before the month as above. They will be applied along the dimension `time_dim` (it is set to 'ftime' by default). - -As mentioned, these parameters are optional, the function will take the entire timeseries when the period is not specified in `start` and `end`. - -The dimensions of SprR forecasts and observations are: - - ``` -dim(SprR_exp$data) -#dataset member sdate lat lon -# 1 3 4 4 4 -dim(SprR_obs$data) -#dataset member sdate lat lon -# 1 1 4 4 4 -``` - -The forecast SprR for the 1st member from 2013-2016 of the 1st grid point in mm are: - - ``` -SprR_exp$data[1,1,,1,1] * 86400 * 1000 -#[1] 93.23205 230.41904 194.01412 226.52614 -``` - -Dry springs will delay vegetative growth and reduce vigour and leaf area total surface. Fungal disease pressure will be lower and therefore there will be less need for protective and / or curative treatments, translating as less costs. Wet springs will promote higher vigour, increase the risk of fungal disease and disrupt vineyard operations as it may prevent machinery from getting in the vineyard due to mud. They are usually associated with higher costs. - -On the other hand, another moisture-related indicators, **HarvestR**, can be computed by using `PeriodAccumulation` as well, with the defined period as the following lines. - -``` -HarvestR_exp <- CST_PeriodAccumulation(prlr_exp, start = list(21, 8), end = list(21, 10)) -HarvestR_obs <- CST_PeriodAccumulation(prlr_obs, start = list(21, 8), end = list(21, 10)) -``` - -The forecast HarvestR for the 1st member from 2013-2016 of the 1st grid point in mm are: - - ``` -HarvestR_exp$data[1,1,,1,1] * 86400 * 1000 -#[1] 52.30026 42.88068 156.87961 32.18579 -``` - -To compute the 2013-2016 ensemble-mean bias of forecast HarvestR, run - -``` -fcst <- drop(HarvestR_exp$data) * 86400 * 1000 -obs <- drop(HarvestR_obs$data) * 86400 * 1000 - -Bias <- MeanDims((fcst - InsertDim(obs, 1, dim(fcst)['member'])), 'member') -``` - -To plot the map of ensemble-mean bias of HarvestR forecast, run - -``` -cols <- c('#b2182b', '#d6604d', '#f4a582', '#fddbc7', '#d1e5f0', - '#92c5de', '#4393c3', '#2166ac') - -PlotEquiMap(Bias[1,,], lon = prlr_obs$lon, lat = prlr_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'mm', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], - margin_scale = c(1, 1, 1, 1), cols = cols[2:7], col_sup = cols[8], - brks = seq(-60, 60, 20), colNA = 'white', - toptitle = 'Ensemble-mean bias of HarvestR in 2013', - bar_label_scale = 1.5, bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) -``` -You will see the following maps of HarvestR bias in 2013. - - - - In 2013, the ensemble-mean SEAS5 seasonal forecast of HarvestR is underestimated by up to 60 mm over Douro Valley region (the central four grid points). - - -### 2. PeriodMean - -For the function `PeriodMean`, we use Growing Season Temperature (**GST**) as an example. GST is defined as the average of daily average temperatures between April 1st to October 31st in the Northern Hemisphere. It provides information onto which are the best suited varieties for a given site or, inversely, which are the best places to grow a specific variety. For existing vineyards, GST also informs on the suitability of its varieties for the climate of specific years, explaining quality and production variation. Many grapevine varieties across the world have been characterized in function of their GST optimum. - -Firstly, we prepare a sample data of daily mean temperature of SEAS5 and ERA5 data sets with the same starting dates, spatial domain, interpolation grid and method by running - -``` -S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$YEAR$$MONTH$01.nc') -ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$_mean/$VAR_NAME$_f1h-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') - -c(tas_exp, tas_obs) %<-% CST_Load(var = 'tas', exp = list(S5path), obs = list(ERA5path), - sdates = sdates, lonmax = 353, lonmin = 352.25, - latmax = 41.75, latmin = 41, - storefreq = 'daily', - leadtimemin = 1, leadtimemax = 214, - nmember = 3, output = "lonlat", - grid = "r1440x721", method = 'bicubic') -``` - -The output contains observations `tas_dv$obs$data` and forecast `tas_dv$exp$data`, and their dimensions and summaries are like - -``` -dim(tas_obs$data) -#dataset member sdate ftime lat lon -# 1 1 4 214 4 4 - -dim(tas_exp$data) -#dataset member sdate ftime lat lon -# 1 3 4 214 4 4 - -summary(tas_obs$data - 273.15) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 3.63 13.97 17.25 17.29 20.75 30.21 - -summary(tas_exp$data - 273.15) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 0.54 11.65 16.56 16.50 21.25 31.41 - -``` -To compute the GST for both observation and forecast, run the following lines - -``` -# change the unit of temperature from °C to K - -tas_exp$data <- tas_exp$data - 273.15 -tas_obs$data <- tas_obs$data - 273.15 - -# compute GST - -GST_exp <- CST_PeriodMean(tas_exp, start = list(1, 4), end = list(31, 10)) -GST_obs <- CST_PeriodMean(tas_obs, start = list(1, 4), end = list(31, 10)) - -``` - -Since the period considered for GST is the entire period for starting month of April, in this case the `start` and `end` parameters could be ignored. - -The summaries and dimensions of the output are as follows: - - ``` -summary(GST_exp$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 14.23 15.78 16.50 16.50 17.17 18.70 - -summary(GST_obs$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 15.34 16.77 17.22 17.29 18.00 18.75 - -dim(GST_exp$data) -#dataset member sdate lat lon -# 1 3 4 4 4 - -dim(GST_obs$data) -#dataset member sdate lat lon -# 1 1 4 4 4 -``` - -Here, we plot the 2013-2016 mean climatology of ERA5 GST by running - -``` -# compute ERA5 GST climatology -GST_Clim <- MeanDims(drop(GST_obs$data), 'sdate') - -cols <- c('#ffffd4','#fee391','#fec44f','#fe9929','#ec7014','#cc4c02','#8c2d04') -PlotEquiMap(GST_Clim, lon = tas_obs$lon, lat = tas_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = '°C', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, col_inf = cols[1], - margin_scale = c(1, 1, 1, 1), cols = cols[2:6], col_sup = cols[7], - brks = seq(16, 18.5, 0.5), colNA = 'white', bar_label_scale = 1.5, - toptitle = '2013-2016 mean ERA5 GST', - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) -``` - -The ERA5 GST climatology is shown as below. - - - - ERA5 GST ranges from 17-18.5°C over the Douro Valley region for the period from 2013-2016 as shown in the figure. - - -### 3. TotalTimeExceedingThreshold - -For the function `TotalTimeExceedingThreshold`, **SU35** (Number of Heat Stress Days - 35°C) is taken as an example here. 35°C is the average established threshold for photosynthesis to occur in the grapevine. Above this temperature, the plant closes its stomata. If this situation occurs after veraison, maturation will be arrested for as long as the situation holds, decreasing sugar, polyphenol and aroma precursor levels, all essential for grape and wine quality. The higher the index, the lower will be the berry quality and aptitude to produce quality grapes. - -SU35 is defined as the Total count of days when daily maximum temperatures exceed 35°C in the seven months into the future. There are three indicators sharing the similar definition as SU35: SU36, SU40 and Spr32. Their definition are listed as follows. - -1. **SU36**: Total count of days when daily maximum temperatures exceed 36°C between June 21st and September 21st -2. **SU40**: Total count of days when daily maximum temperatures exceed 40°C between June 21st and September 21st -3. **Spr32**: Total count of days when daily maximum temperatures exceed 32°C between April 21st and June 21st - -These indicators can be computed as well by using the function `TotalTimeExceedingThreshold` with different thresholds and periods indicated. - -Here, we take SU35 as example, therefore the daily temperature maximum of the entire 7-month forecast period is needed for the computation of this indicator. - -Load SEAS5 and ERA5 daily temperature maximum by running - -``` -S5path <- list(path = '/esarchive/exp/ecmwf/system5c3s/$STORE_FREQ$/$VAR_NAME$/$VAR_NAME$_$YEAR$$MONTH$01.nc') -ERA5path <- list(path = '/esarchive/recon/ecmwf/era5/$STORE_FREQ$/$VAR_NAME$-r1440x721cds/$VAR_NAME$_$YEAR$$MONTH$.nc') - -c(tasmax_exp, tasmax_obs) %<-% CST_Load(var = 'tasmax', exp = list(S5path), - obs = list(ERA5path), sdates = sdates, - lonmax = 353, lonmin = 352.25, - latmax = 41.75, latmin = 41, storefreq = 'daily', - leadtimemin = 1, leadtimemax = 214, nmember = 3, - output = "lonlat", grid = "r1440x721", - method = 'bicubic', nprocs = 1) -``` - -Check the unit of temperature to from °C to K for the comparison with the threshold defined (for example 35°C here). - -``` -tasmax_exp$data <- tasmax_exp$data - 273.15 -tasmax_obs$data <- tasmax_obs$data - 273.15 -``` - -Computing SU35 for forecast and observation by running - -``` -threshold <- 35 -SU35_exp <- CST_TotalTimeExceedingThreshold(tasmax_exp, threshold = threshold, - start = list(1, 4), end = list(31, 10)) -SU35_obs <- CST_TotalTimeExceedingThreshold(tasmax_obs, threshold = threshold, - start = list(1, 4), end = list(31, 10)) -``` - -The summaries of SU35 forecasts and observations are given below. - -``` -summary(SU35_exp$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 0.00 2.00 5.00 7.12 12.00 26.00 - -summary(SU35_obs$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 0.000 0.000 1.000 2.609 5.000 10.000 - -``` -As shown in the summaries, SEAS5 SU35 forecasts are overestimated by 5 days in terms of mean value. - -Therefore, `CST_BiasCorrection` is used to bias adjust the SU35 forecasts. - -``` -res <- CST_BiasCorrection(obs = SU35_obs, exp = SU35_exp) -SU35_exp_BC <- drop(res$data) -summary(SU35_exp_BC) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# -1.419 0.000 1.613 2.831 4.756 17.768 -``` - -Since there are negative values after bias adjustment, all negative data is converted to zero. - -``` -SU35_exp_BC[SU35_exp_BC < 0] <- 0 -summary(SU35_exp_BC) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 0.000 0.000 1.613 2.943 4.756 17.768 -``` - -Plot the bias-adjusted SU35 forecast in 2016 by running - -``` -SU35_obs_Y2016 <- drop(SU35_obs$data)[4, , ] -SU35_exp_Y2016 <- MeanDims(drop(SU35_exp$data)[, 4, , ], 'member') -SU35_exp_BC_Y2016 <- MeanDims(SU35_exp_BC[, 4, , ], 'member') -cols <- c("#fee5d9", "#fcae91", "#fb6a4a", "#de2d26","#a50f15") - -toptitle <- 'ERA5 SU35 forecast in 2016' -PlotEquiMap(SU35_obs_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'day', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), - cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), - toptitle = toptitle, - colNA = cols[1], bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - -toptitle <- 'SU35 forecast in 2016' -PlotEquiMap(SU35_exp_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'day', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), - cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), - toptitle = toptitle, - colNA = cols[1], bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - -toptitle <- 'Bias-adjusted SU35 forecast in 2016' -PlotEquiMap(SU35_exp_BC_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'day', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), - cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), - toptitle = toptitle, - colNA = cols[1], bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) -``` -You can see the figure as below. - - - - - - - As seen above, the bias-adjusted SU35 forecasts are much closer to the ERA5 results, although differences remain. - -Beside of the original definition of SU35, here two supplementary functions in the package `CSIndicators` are demonstrated by computing its another definition with the percentile adjustment. - ---- - 1. **AbsToProbs -**: to transform ensemble forecast into probabilities by using the Cumulative Distribution Function -2. **QThreshold -**: to transform an absolute threshold into probabilities. ---- - - The above two supplementary functions are required to compute SU35 with the percentile adjustment. The function `AbsToProbs` would be applied to forecast and the `QThreshold` would be used to convert the observations to its percentile based on the given threshold. - -The revised definition of SU35 is to reduce the potential influence induced by the fixed threshold of temperature defined for the index, instead of using the absolute value, the percentile corresponding to 35°C for observation is compared to the percentile corresponding to the predicted daily maximum temperature before being considered as a ‘heat stress’ day. - -As mentioned, the forecast is translated to its percentile by using the function `ABsToProbs` by running - -``` -exp_percentile <- AbsToProbs(tasmax_exp$data) -S5txP <- aperm(drop(exp_percentile), c(2, 1, 3, 4, 5)) -``` - -After that, based on 35 of threshold, the percentile corresponding to each observational value can be calculated as follows. - -``` -obs_percentile <- QThreshold(tasmax_obs$data, threshold = 35) -obs_percentile <- drop(obs_percentile) -``` - -After translating both forecasts and observations into probabilities, the comparison can then be done by running - -``` -SU35_exp_Percentile <- TotalTimeExceedingThreshold(S5txP, threshold = obs_percentile, time_dim = 'ftime') -``` - -Compute the same ensemble-mean SU35 **with percentile adjustment** in 2016 by running - -``` -SU35_exp_per_Y2016 <- MeanDims(SU35_exp_Percentile[, 4, , ], 'member') -``` - -Plot the same map for comparison - -``` -toptitle <- 'SU35 forecast with percentile adjustment in 2016' -PlotEquiMap(SU35_exp_per_Y2016, lon = tasmax_obs$lon, lat = tasmax_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'day', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), - cols = cols[1:4], col_sup = cols[5], brks = seq(0, 8, 2), - toptitle = toptitle, - colNA = cols[1], bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) - -``` - - - - - As seen in the figure above, applying the percentile adjustment seems to implicitly adjust certain extent of bias which was observed in the non-bias-adjusted SEAS5 forecast. - -The performance of comparison of skills between two definitions requires further analysis such as the application of more skill metrics. - -### 4. AccumulationExceedingThreshold - -The function ´AccumulationExceedingThreshold´ can compute GDD (Growing Degree Days). - -The definition of GDD is the summation of daily differences between daily average temperatures and 10°C between April 1st and October 31st. Here, the tas (daily average temperature) used above (in Section 2. PeriodMean) is loaded again (Please re-use the section of loading tas in Section 2). As per the definition, `threshold` is set to 10 with `diff` set to TRUE so that the function will compute the differences between daily temperature and the threshold given before calculating summation. - -*Note: The data is in degrees Celsiusi at this point* - - ``` -GDD_exp <- CST_AccumulationExceedingThreshold(tas_exp, threshold = 10, diff = TRUE) -GDD_obs <- CST_AccumulationExceedingThreshold(tas_obs, threshold = 10, diff = TRUE) -``` - -The summaries of GDD are - -``` -summary(GDD_exp$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 1021 1331 1480 1469 1596 1873 - -summary(GDD_obs$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 1195 1483 1569 1583 1730 1874 -``` - -To compute the correlation coefficient for the period from 2013-2016, run the following lines - -``` -# reorder the dimension -fcst <- Reorder(drop(GDD_exp$data), c(4, 3, 2, 1)) -obs <- Reorder(drop(GDD_obs$data), c(3, 2, 1)) - -EnsCorr <- veriApply('EnsCorr', fcst = fcst, obs = obs, ensdim = 4, tdim = 3) -GDD_Corr <- Reorder(EnsCorr, c(2, 1)) -``` - -To plot the map of correlation coefficient of GDD for the 2013-2016 period. - -``` -cols <- c("#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476") -toptitle <- '2013-2016 correlation coefficient of GDD' -PlotEquiMap(GDD_Corr, lon = tas_obs$lon, lat = tas_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'correlation', - title_scale = .8, axes_label_scale = 1, axes_tick_scale = 1, - margin_scale = c(1, 1, 1, 1), cols = cols, brks = seq(0.5, 1, 0.1), - toptitle = toptitle, bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) -``` - -The map of correlation coefficient for the 2013-2016 period is shown as below. - - - - The 2013-2016 correlation coefficients of the SEAS5 forecasts of GDD in reference with ERA5 reanalysis over Douro Valley range between 0.6 and 0.8. - -### 5. TotalSpellTimeExceedingThreshold - -One of the critical agricultural indicators related to dry spell is the **Warm Spell Duration Index (WSDI)**, which is defined as the total count of days with at least 6 consecutive days when the daily maximum temperature exceeds its 90th percentile in the seven months into the future. - -The maximum temperature data used in Section 3. Since the daily maximum temperature needs to compare to its 90th percentile, the function `Threshold` in the `CSIndicators` package is required to compute the percentile of observations used for each day. Here the same period (2013-2016) is considered. - -``` -tx_p <- CST_Threshold(tasmax_obs, threshold = 0.9) -``` - -The output will be the 90th percentile of each day of each grid point derived by using all the years in the data.See the dimension and summary as below. - -``` -dim(tx_p$data) -#dataset ftime lat lon -# 1 214 4 4 - -summary(tx_p$data) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 13.83 22.08 26.08 26.22 30.72 36.72 -``` - -With the prepared threshold (90th percentile), the WSDI can be computed by running - -``` -WSDI_exp <- CST_TotalSpellTimeExceedingThreshold(tasmax_exp, threshold = tx_p, spell = 6) -WSDI_obs <- CST_TotalSpellTimeExceedingThreshold(tasmax_obs, threshold = tx_p, spell = 6) -``` - -After checking the summaries, compute the Fair Ranked Probability Skill Score (FRPSS) of WSDI by running the following lines - -``` -# Reorder the data -fcst <- Reorder(drop(WSDI_exp$data), c(4, 3, 2, 1)) -obs <- Reorder(drop(WSDI_obs$data), c(3, 2, 1)) - -# summaries of WSDI -summary(fcst) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 0.00 13.00 28.00 31.22 46.00 82.00 - -summary(obs) -# Min. 1st Qu. Median Mean 3rd Qu. Max. -# 9.00 19.00 22.50 23.16 26.00 33.00 - -# compute FRPSS -f <- veriApply('FairRpss', fcst = fcst, obs = obs, ensdim = 4, tdim = 3, prob = 1:2/3)$skillscore -WSDI_FRPSS <- Reorder(f, c(2,1)) -``` - -Plot the map of WSDI FRPSS for the period from 2013-2016 - -``` -cols <- c("#edf8fb", "#ccece6", "#99d8c9", "#66c2a4") -toptitle <- 'SEAS5 WSDI FRPSS (2013-2016)' - -PlotEquiMap(WSDI_FRPSS, lon = tasmax_obs$lon, lat = tasmax_obs$lat, - intylat = 1, intxlon = 1, width = 6, height = 6, - filled.continents = FALSE, units = 'FRPSS', title_scale = .8, - axes_label_scale = 1, axes_tick_scale = 1, margin_scale = c(1, 1, 1, 1), - cols = cols[1:3], col_inf = 'white', col_sup = cols[4], - brks = seq(0, 0.9, 0.3), toptitle = toptitle, bar_label_scale = 1.5, - bar_extra_margin = c(0, 0, 0, 0), units_scale = 2) -``` - -The FRPSS map for 2013-2016 SEAS WSDI is shown as below. - - - - As seen in the map, the FRPSS in the eastern part of Douro Valley falls in 0.6-0.9, which are good enough to be useful when compared to observational climatology. - - -In addition to the grape/wine sector focused here, the MEDGOLD project also works on the other two sectors: olive/olive oil and durum wheat/pasta. Furthermore, the climate services are also provided at the longer term (up to 30 years) by other project partners. - -Click on [MEDGOLD](https://www.med-gold.eu/climate-services/) for more information. - - - -- GitLab From 2e0fa0708b30835a094383756fbbf233e75ddaeb Mon Sep 17 00:00:00 2001 From: mlotto Date: Sun, 28 Aug 2022 16:48:38 +0200 Subject: [PATCH 05/29] add references --- vignettes/malaria/MalariaIndicator.Rmd | 37 ++++++++++++++++--------- vignettes/malaria/MalariaIndicator.html | 21 ++++++++++---- 2 files changed, 40 insertions(+), 18 deletions(-) diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd index 9e43f52..e96e231 100644 --- a/vignettes/malaria/MalariaIndicator.Rmd +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -28,22 +28,22 @@ Climate Suitability for Malaria Transmission Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ -mosquitoes. Eradication efforts managed to achieve complete elimination of endemic +mosquitoes (77). Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions -and increased life expectancy are key (ref). On the other hand, climatic conditions are -becoming more suitable for malaria transmission (ref). +and increased life expectancy are key (78). On the other hand, climatic conditions are +becoming more suitable for malaria transmission (79). The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the -dynamic of different health processes (ref). As part of this initiative, the Climate +dynamic of different health processes (Lancet Europe launch). As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021. This indicator is a threshold based model that overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and _Plasmodium vivax_, which was the parasite of endemic -circulation in the continent until the mid 1970s. +circulation in the continent until the mid 1970s (77). Although the `CSIndicators` was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand @@ -52,11 +52,6 @@ the Climate Suitability for Malaria indicator will be computed using the `Tota array containing the number of months per year suitable for malaria transmission. - -(For further information about the different types of objects visit the section -about [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html) -from CSTools package ). - ## Load libraries In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will @@ -88,10 +83,10 @@ knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/m ### 1.2 Data extraction Malaria indicator is computed with climate and environmental data. Climate variables -are extracted from the ERA5-Land reanalysis dataset (ref), which span between 1950 +are extracted from the ERA5-Land reanalysis dataset (89), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation - (`prlr`). Environmental information is extracted from the CORINE Land Cover inventory, + (`prlr`). Environmental information is extracted from the CORINE Land Cover inventory (https://land.copernicus.eu/pan-european/corine-land-cover), which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**. @@ -218,6 +213,20 @@ summary(out$prlr$data) Add chunk ## 3. Compute indicator +The suitability for malaria transmission indicator estimated the number of months +suitable for transmission of P. vivax, calculated from the following thresholds: + +* Accumulated monthly precipitaiton above 80 mm (81), +* Monthly temperature between 14.5ºC and 33ºC (80), and +* Monthly relative humidity above 60% (80) + +Additionally, _Anopheles_ suitability is influenced by the surrounding conditions, so that the +following land cover classes are considered highly suitable (82): + +* Rice fields, +* Permanently irrigated croplands, and +* Sport and leisure facilities + ```{r} # Create a template with the same dimensions as the datasets malariaSuit <- out$tas @@ -283,4 +292,6 @@ Add chunk Add chunk ## References -Add text \ No newline at end of file +77 +78 +79 \ No newline at end of file diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html index f627289..129db3f 100644 --- a/vignettes/malaria/MalariaIndicator.html +++ b/vignettes/malaria/MalariaIndicator.html @@ -350,10 +350,9 @@ text-align: justify

Introduction

-

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes. Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (ref). On the other hand, climatic conditions are becoming more suitable for malaria transmission (ref).

-

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes (ref). As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021. This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasite of endemic circulation in the continent until the mid 1970s.

+

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (77). Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (78). On the other hand, climatic conditions are becoming more suitable for malaria transmission (79).

+

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes (Lancet Europe launch). As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021. This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasite of endemic circulation in the continent until the mid 1970s (77).

Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

-

(For further information about the different types of objects visit the section about Data retrieval and storage from CSTools package ).

Load libraries

@@ -372,7 +371,7 @@ text-align: justify

1.2 Data extraction

-

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (ref), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory, which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

+

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (89), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory (https://land.copernicus.eu/pan-european/corine-land-cover), which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

The function CST_Load from the CSTools package allows the user to access the processed data and delivers the data in the form of an s2dv_cube object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit Data retrieval and storage.

CST_Load has its requirements for extracting data. First, the path to the files is introduced using whitecards delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with sdates, which takes a vector of all dates of interest. The spatial extent is inputted within lonmax, lonmin, latmax and latmin.

# Path to file locations using whitecards
@@ -486,6 +485,18 @@ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}

3. Compute indicator

+

The suitability for malaria transmission indicator estimated the number of months suitable for transmission of P. vivax, calculated from the following thresholds:

+
    +
  • Accumulated monthly precipitaiton above 80 mm (81),
    • +
  • Monthly temperature between 14.5ºC and 33ºC (80), and
    • +
  • Monthly relative humidity above 60% (80)
    • +
+

Additionally, Anopheles suitability is influenced by the surrounding conditions, so that the following land cover classes are considered highly suitable (82):

+
    +
  • Rice fields,
    • +
  • Permanently irrigated croplands, and
    • +
  • Sport and leisure facilities
    • +
# Create a template with the same dimensions as the datasets
 malariaSuit <- out$tas
 
@@ -547,7 +558,7 @@ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}

References

-

Add text

+

77 78 79

-- GitLab From 93c9e8cd3eeb50ccaa1876a995d3bca048225125 Mon Sep 17 00:00:00 2001 From: mlotto Date: Mon, 29 Aug 2022 12:34:04 +0200 Subject: [PATCH 06/29] add references --- vignettes/malaria/MalariaIndicator.Rmd | 71 ++++++++++++++----------- vignettes/malaria/MalariaIndicator.html | 36 ++++++++----- 2 files changed, 64 insertions(+), 43 deletions(-) diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd index e96e231..3886e81 100644 --- a/vignettes/malaria/MalariaIndicator.Rmd +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -28,28 +28,31 @@ Climate Suitability for Malaria Transmission Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ -mosquitoes (77). Eradication efforts managed to achieve complete elimination of endemic -malaria in Europe since 1974, although sporadic transmission events are frequently -reported in travelers coming from other endemic areas. Studies evaluating the factors -that keep malaria out of the continent found that high socio-economic conditions -and increased life expectancy are key (78). On the other hand, climatic conditions are -becoming more suitable for malaria transmission (79). - -The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose -of using a variety of indicators to monitor trends in the impact of climate on the -dynamic of different health processes (Lancet Europe launch). As part of this initiative, the Climate -Suitability for Malaria Transmission indicator was adapted from the Global Lancet -Countdown Collaboration for tracking the number of months suitable of malaria -transmission per year in the continent between 1950 and 2021. This indicator is a -threshold based model that overlaps the climatic and environmental requirements of -_Anopheles_ mosquitoes and _Plasmodium vivax_, which was the parasite of endemic -circulation in the continent until the mid 1970s (77). +mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete +elimination of endemic malaria in Europe since 1974, although sporadic transmission +events are frequently reported in travelers coming from other endemic areas. Studies +evaluating the factors that keep malaria out of the continent found that high socio-economic +conditions and increased life expectancy are key (Fischer et al., 2020). On the +other hand, climatic conditions are becoming more suitable for malaria +transmission (Zhao et al., 2016). + +The [Lancet Countdown in Europe collaboration]("https://www.lancetcountdown.org/europe/") +was launched in 2021 with the purpose of using a variety of indicators to monitor +trends in the impact of climate on the dynamic of different health processes. As +part of thisinitiative, the Climate Suitability for Malaria Transmission indicator +was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) +for tracking the number of months suitable of malaria transmission per year in the +continent between 1950 and 2021.This indicator is a threshold based model that +overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and +_Plasmodium vivax_, which was the parasiteof endemic circulation in the continent +until the mid 1970s (Boualam et al., 2021). Although the `CSIndicators` was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, -the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold` function. The output will be an multidimensional -array containing the number of months per year suitable for malaria transmission. +the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold` +function. The output will be an multidimensional array containing the number of +months per year suitable for malaria transmission. ## Load libraries @@ -83,10 +86,10 @@ knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/m ### 1.2 Data extraction Malaria indicator is computed with climate and environmental data. Climate variables -are extracted from the ERA5-Land reanalysis dataset (89), which span between 1950 +are extracted from the [ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation - (`prlr`). Environmental information is extracted from the CORINE Land Cover inventory (https://land.copernicus.eu/pan-european/corine-land-cover), + (`prlr`). Environmental information is extracted from the [CORINE Land Cover inventory]("https://land.copernicus.eu/pan-european/corine-land-cover"), which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**. @@ -176,12 +179,12 @@ summary(out$tas$data) ### 2.3 Relative humidity After transforming temperature ($T$) and dew point temperature ($T_d$) units from Kelvin (K) to Degrees Celsius (C), it is possible to compute the relative humidity using -August-Roche-Magnus equation (ref): +August-Roche-Magnus equation (Alduchov 1996): $$ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} $$ -Where a and b are the coefficients 17.625 and 243.04, respectively. +Where _a_ and _b_ are the coefficients 17.625 and 243.04, respectively. ```{r} # Create a template with the same dimensions as the datasets @@ -214,14 +217,14 @@ Add chunk ## 3. Compute indicator The suitability for malaria transmission indicator estimated the number of months -suitable for transmission of P. vivax, calculated from the following thresholds: +suitable for transmission of _P. vivax_, calculated from the following thresholds: -* Accumulated monthly precipitaiton above 80 mm (81), -* Monthly temperature between 14.5ºC and 33ºC (80), and -* Monthly relative humidity above 60% (80) +* Accumulated monthly precipitation above 80 mm (Lyon et al., 2017), +* Monthly temperature between 14.5ºC and 33ºC (Grover-Kopec et al., 2006), and +* Monthly relative humidity above 60% (Grover-Kopec et al., 2006) Additionally, _Anopheles_ suitability is influenced by the surrounding conditions, so that the -following land cover classes are considered highly suitable (82): +following land cover classes are considered highly suitable (Benali et al., 2014): * Rice fields, * Permanently irrigated croplands, and @@ -292,6 +295,14 @@ Add chunk Add chunk ## References -77 -78 -79 \ No newline at end of file + +* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL]("https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml") +* Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. _Remote Sens Environ. 2014;145:116-130._ DOI:10.1016/J.RSE.2014.01.014 +* Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. _Front Med. 2021; 8:876._ DOI:10.3389/FMED.2021.691095/BIBTEX +* Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. _Travel Med Infect Dis. 2020;36:101815._ DOI:10.1016/J.TMAID.2020.101815 +* Grover-Kopec EK, Blumenthal MB, Ceccato P, Dinku T, Omumbo JA, Connor SJ. Web-based climate information resources for malaria control in Africa. _Malar J. 2006;5(1):1-9._ DOI:10.1186/1475-2875-5-38/FIGURES/5 +* Lyon B, Dinku T, Raman A, Thomson MC. Temperature suitability for malaria climbing the Ethiopian Highlands. _Environ Res Lett. 2017;12(6):064015._ DOI:10.1088/1748-9326/AA64E6 +* Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. _Earth Syst Sci Data. 2021;13(9):4349-4383._ DOI:10.5194/ESSD-13-4349-2021 +* Romanello M, McGushin A, Di Napoli C, et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. _Lancet. 2021;398(10311):1619-1662._ DOI:https://doi.org/10.1016/S0140-6736(21)01787-6 +* Zhao X, Smith DL, Tatem AJ. Exploring the spatiotemporal drivers of malaria elimination in Europe. _Malar J. 2016;15(1):1-13._ DOI:10.1186/S12936-016-1175-Z/TABLES/5 + diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html index 129db3f..1fafc7a 100644 --- a/vignettes/malaria/MalariaIndicator.html +++ b/vignettes/malaria/MalariaIndicator.html @@ -12,7 +12,7 @@ - + Malaria Suitability Indicator @@ -336,7 +336,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }

Malaria Suitability Indicator

Earth Sciences department, Barcelona Supercomputing Center (BSC)

-

2022-08-28

+

2022-08-29

@@ -350,8 +350,8 @@ text-align: justify

Introduction

-

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (77). Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (78). On the other hand, climatic conditions are becoming more suitable for malaria transmission (79).

-

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes (Lancet Europe launch). As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021. This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasite of endemic circulation in the continent until the mid 1970s (77).

+

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (Fischer et al., 2020). On the other hand, climatic conditions are becoming more suitable for malaria transmission (Zhao et al., 2016).

+

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes. As part of thisinitiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasiteof endemic circulation in the continent until the mid 1970s (Boualam et al., 2021).

Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

@@ -371,7 +371,7 @@ text-align: justify

1.2 Data extraction

-

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (89), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory (https://land.copernicus.eu/pan-european/corine-land-cover), which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

+

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory, which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

The function CST_Load from the CSTools package allows the user to access the processed data and delivers the data in the form of an s2dv_cube object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit Data retrieval and storage.

CST_Load has its requirements for extracting data. First, the path to the files is introduced using whitecards delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with sdates, which takes a vector of all dates of interest. The spatial extent is inputted within lonmax, lonmin, latmax and latmin.

# Path to file locations using whitecards
@@ -449,10 +449,10 @@ text-align: justify

2.3 Relative humidity

-

After transforming temperature (\(T\)) and dew point temperature (\(T_d\)) units from Kelvin (K) to Degrees Celsius (C), it is possible to compute the relative humidity using August-Roche-Magnus equation (ref):

+

After transforming temperature (\(T\)) and dew point temperature (\(T_d\)) units from Kelvin (K) to Degrees Celsius (C), it is possible to compute the relative humidity using August-Roche-Magnus equation (Alduchov 1996):

\[ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} -\] Where a and b are the coefficients 17.625 and 243.04, respectively.

+\] Where a and b are the coefficients 17.625 and 243.04, respectively.

# Create a template with the same dimensions as the datasets
 out$rh <- out$tas
 
@@ -485,13 +485,13 @@ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}

3. Compute indicator

-

The suitability for malaria transmission indicator estimated the number of months suitable for transmission of P. vivax, calculated from the following thresholds:

+

The suitability for malaria transmission indicator estimated the number of months suitable for transmission of P. vivax, calculated from the following thresholds:

    -
  • Accumulated monthly precipitaiton above 80 mm (81),
    • -
  • Monthly temperature between 14.5ºC and 33ºC (80), and
    • -
  • Monthly relative humidity above 60% (80)
    • +
  • Accumulated monthly precipitation above 80 mm (Lyon et al., 2017),
    • +
  • Monthly temperature between 14.5ºC and 33ºC (Grover-Kopec et al., 2006), and
    • +
  • Monthly relative humidity above 60% (Grover-Kopec et al., 2006)
-

Additionally, Anopheles suitability is influenced by the surrounding conditions, so that the following land cover classes are considered highly suitable (82):

+

Additionally, Anopheles suitability is influenced by the surrounding conditions, so that the following land cover classes are considered highly suitable (Benali et al., 2014):

  • Rice fields,
  • Permanently irrigated croplands, and
    • @@ -558,7 +558,17 @@ RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}

References

-

77 78 79

+
    +
  • Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. J Appl Meteorl Climatol. 1996; 35(4),601-609. URL
    • +
  • Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. Remote Sens Environ. 2014;145:116-130. DOI:10.1016/J.RSE.2014.01.014
    • +
  • Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. Front Med. 2021; 8:876. DOI:10.3389/FMED.2021.691095/BIBTEX
    • +
  • Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. Travel Med Infect Dis. 2020;36:101815. DOI:10.1016/J.TMAID.2020.101815
    • +
  • Grover-Kopec EK, Blumenthal MB, Ceccato P, Dinku T, Omumbo JA, Connor SJ. Web-based climate information resources for malaria control in Africa. Malar J. 2006;5(1):1-9. DOI:10.1186/1475-2875-5-38/FIGURES/5
    • +
  • Lyon B, Dinku T, Raman A, Thomson MC. Temperature suitability for malaria climbing the Ethiopian Highlands. Environ Res Lett. 2017;12(6):064015. DOI:10.1088/1748-9326/AA64E6
    • +
  • Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data. 2021;13(9):4349-4383. DOI:10.5194/ESSD-13-4349-2021
    • +
  • Romanello M, McGushin A, Di Napoli C, et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet. 2021;398(10311):1619-1662. DOI:https://doi.org/10.1016/S0140-6736(21)01787-6
    • +
  • Zhao X, Smith DL, Tatem AJ. Exploring the spatiotemporal drivers of malaria elimination in Europe. Malar J. 2016;15(1):1-13. DOI:10.1186/S12936-016-1175-Z/TABLES/5
    • +
-- GitLab From 98ebdab0c7de765b8852543b6c4466cfc06eec1f Mon Sep 17 00:00:00 2001 From: mlotto Date: Tue, 30 Aug 2022 17:57:48 +0200 Subject: [PATCH 07/29] first draft vignette --- vignettes/malaria/MalariaIndicator.Rmd | 192 +++++++++++++--------- vignettes/malaria/MalariaIndicator.html | 209 +++++++++++++----------- vignettes/malaria/call_data.R | 90 ---------- vignettes/malaria/ind_computation.R | 134 --------------- vignettes/malaria/make_figs.R | 3 - 5 files changed, 227 insertions(+), 401 deletions(-) delete mode 100644 vignettes/malaria/call_data.R delete mode 100644 vignettes/malaria/ind_computation.R delete mode 100644 vignettes/malaria/make_figs.R diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd index 3886e81..11b5b71 100644 --- a/vignettes/malaria/MalariaIndicator.Rmd +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -16,7 +16,7 @@ text-align: justify ```{r echo=FALSE} -knitr::opts_chunk$set(warnings=FALSE, message=FALSE) +knitr::opts_chunk$set(warning=FALSE, message=FALSE) ``` Climate Suitability for Malaria Transmission @@ -25,32 +25,34 @@ Climate Suitability for Malaria Transmission ## Introduction -Climate sensitive infectious diseases are a subject of major concern for public -health around the world in the context of global change. Malaria is an infectious +Climate sensitive infectious diseases are a subject of major concern in the context +of a changing climate. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete -elimination of endemic malaria in Europe since 1974, although sporadic transmission -events are frequently reported in travelers coming from other endemic areas. Studies -evaluating the factors that keep malaria out of the continent found that high socio-economic -conditions and increased life expectancy are key (Fischer et al., 2020). On the -other hand, climatic conditions are becoming more suitable for malaria -transmission (Zhao et al., 2016). +elimination of endemic circulation of malaria in Europe, although sporadic transmission +events are frequently reported in travelers coming from endemic areas. Studies +evaluating drivers of malaria elimination found that high socio-economic +conditions and increased life expectancy are key determinants in keeping this disease +out of the continent (Fischer et al., 2020). On the +other hand, the risk of malaria resurgence is present as climatic conditions +become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). The [Lancet Countdown in Europe collaboration]("https://www.lancetcountdown.org/europe/") was launched in 2021 with the purpose of using a variety of indicators to monitor -trends in the impact of climate on the dynamic of different health processes. As -part of thisinitiative, the Climate Suitability for Malaria Transmission indicator -was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) -for tracking the number of months suitable of malaria transmission per year in the -continent between 1950 and 2021.This indicator is a threshold based model that +trends in the impact of climate on dynamic of different health processes. As +part of this initiative, the Climate Suitability for Malaria Transmission indicator +was adapted from the [Global Lancet Countdown Collaboration]("https://www.lancetcountdown.org/") +(Romanello et al., 2021) +to track the annual number of months suitable of malaria transmission between 1950 +and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and -_Plasmodium vivax_, which was the parasiteof endemic circulation in the continent +_Plasmodium vivax_, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021). Although the `CSIndicators` was initially developed for providing a set of tools -useful in the context of agricultural applications, it is also possible to expand +useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, -the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold` +the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold()` function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission. @@ -77,20 +79,18 @@ source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from ## 1. Data ### 1.1 Area of interest The number of months suitable for malaria transmission between 2010 and 2020 will -be computed for the Iberian Peninsula (40º14´24´´N 4º14´21´´W). This area comprises -the territories of Portugal, Spain, France, Andorra and Gibraltar. +be computed for the Iberian Peninsula. ```{r echo=FALSE} knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg") ``` ### 1.2 Data extraction -Malaria indicator is computed with climate and environmental data. Climate variables -are extracted from the [ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950 -and present day, at a 9km resolution. Necessary climate variables are 2 meter +The Malaria indicator is computed with climate variables extracted from the +[ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950 +and present day, at a 9km resolution. These variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation - (`prlr`). Environmental information is extracted from the [CORINE Land Cover inventory]("https://land.copernicus.eu/pan-european/corine-land-cover"), -which classifies land into 44 classes at a 100 m resolution. These data are produced + (`prlr`). These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**. @@ -111,8 +111,8 @@ path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$V # Temporal extent and resolution: # We will work with all months from 2010 to 2020 -year_in <- 2020 # initial year -year_fi <- 2021 # last year +year_in <- 2010 # initial year +year_fi <- 2020 # last year month_in <- 1 # first month month_fi <- 12 # last month @@ -127,8 +127,8 @@ for(var in vars){ exp = NULL, # We only require observed data obs = list(path_ERA5_CDS), sdates = sdates, - lonmax = 355, lonmin = 350, - latmax = 45, latmin = 43, + lonmax = 5, lonmin = 350, + latmax = 45, latmin = 35, storefreq = 'monthly', leadtimemin = month_in, leadtimemax = month_fi, @@ -148,8 +148,7 @@ for(var in vars){ # output = "lonlat") ``` -The call above creates a list with the three datasets. Each `s2dv_cube` object has -the following dimensions: +The output object is a list with the three `s2dv_cube` objects with dimensions ```{r} dim(out$tas$data) ``` @@ -166,8 +165,7 @@ out$tas$data[1,1,1,1,,][15:20, 15:20] ## 2. Data transformation ### 2.1 Temperature and dew point temperature -This variables will be used to compute relative humidity, which requires them to -be in Degrees Celsius (C): +First, it is necessary to transform units from Kelvin (K) to Degrees Celsius (C) ```{r} for(var in c('tas', 'tdps')){ out[[var]]$data <- out[[var]]$data - 273.15 @@ -177,84 +175,92 @@ summary(out$tas$data) ``` ### 2.3 Relative humidity -After transforming temperature ($T$) and dew point temperature ($T_d$) units from Kelvin (K) to -Degrees Celsius (C), it is possible to compute the relative humidity using -August-Roche-Magnus equation (Alduchov 1996): +Then, it is possible to calculate relative humidity (hurs) using temperature ($T$) +and dew point temperature ($T_d$) with the August-Roche-Magnus equation (Alduchov 1996): $$ -RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} +RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} $$ Where _a_ and _b_ are the coefficients 17.625 and 243.04, respectively. ```{r} -# Create a template with the same dimensions as the datasets -out$rh <- out$tas - -# Compute relative humidity (RH) -out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / - exp((17.625*out$tas$data) / (243.04+out$tas$data))) - -summary(out$rh$data) +# Create a new s2dv object with relative humidity +out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / + exp((17.625*out$tas$data) / (243.04+out$tas$data))), + lon=out$tas$lon, + lat=out$tas$lat, + Variable=list(varName="rh", level=NULL), + Dates=out$tas$Dates, + Datasets=out$tas$Datasets, + when=Sys.time(), + source_files="see source files of tas, tdps and pr") + +attr(out$hurs$Variable, "units") <- "%" +attr(out$hurs$Variable, "longname") <- "near-surface relative humidity" + +summary(out$hurs$data) ``` ### 2.2 Precipitation -Precipitation is extracted from the hourly reanalaysis dataset and processes as -monthly meters per second. For the computation of the malaria indicator, it is -necessary to transform it into accumulated mm per month: +Precipitation is extracted from the hourly reanalaysis dataset and delivered in +monthly meters per second. The next step is to transform it into accumulated mm +per month ```{r} # To compute the accumulated precipitation per month it is necessary to multiply by: # - total number of seconds in one hour: 3600 # - total number of hours in a day: 24 -# - average number of days in a month: 30.41 +# - average number of days in a month: 365.25/12=30.44 # - change factor from meter to mm: 1000 -out$prlr$data <- out$prlr$data*3600*24*30.41*1000 +out$prlr$data <- out$prlr$data*3600*24*30.44*1000 summary(out$prlr$data) ``` -### 2.4 Land use -Add chunk - ## 3. Compute indicator -The suitability for malaria transmission indicator estimated the number of months -suitable for transmission of _P. vivax_, calculated from the following thresholds: +The suitability for malaria transmission indicator estimates the number of months +suitable for transmission of _P. vivax_ according to the following thresholds: * Accumulated monthly precipitation above 80 mm (Lyon et al., 2017), * Monthly temperature between 14.5ºC and 33ºC (Grover-Kopec et al., 2006), and * Monthly relative humidity above 60% (Grover-Kopec et al., 2006) -Additionally, _Anopheles_ suitability is influenced by the surrounding conditions, so that the -following land cover classes are considered highly suitable (Benali et al., 2014): - -* Rice fields, -* Permanently irrigated croplands, and -* Sport and leisure facilities - ```{r} -# Create a template with the same dimensions as the datasets -malariaSuit <- out$tas - -# Determine suitability -malariaSuit$data <- ifelse(out$tas$data >= 14.5 & - out$tas$data <= 33 & - out$rh$data >= 60 & - out$prlr$data >= 80, 1, 0) +# Create a new s2dv object with malaria suitability +out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & + out$tas$data <= 33 & + out$hurs$data >= 60 & + out$prlr$data >= 80, 1, 0), + lon=out$tas$lon, + lat=out$tas$lat, + Variable=list(varName="malar_suit", level=NULL), + Dates=out$tas$Dates, + Datasets=out$tas$Datasets, + when=Sys.time(), + source_files="see source files of tas, tdps and pr") + +attr(out$malariaSuit$Variable, "units") <- "none" +attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission" # Compute number of number of months per year that are suitable # (i.e. suitability = 1) -malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(malariaSuit, +malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, threshold=0.5) # Sneak peek of the data malariaInd$data[1,1,1,,][15:20, 15:20] + +summary(malariaInd$data) ``` -## 4. Visualization +## 4. Results ### 4.1. Map Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to have a visual inspection of the number of suitable months in January 2010. ```{r echo=FALSE} +# Source master branch for PlotLayout() +source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotLayout.R') + # Until the dependency with s2dverification in CSTools is updated, it is necessary # to make these adjustments source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotEquiMap.R') @@ -266,33 +272,57 @@ clim.palette <- s2dv::clim.palette .IsColor<- s2dv:::.IsColor ``` -```{r fig.width=8, fig.height=4} +```{r fig.width=7, fig.height=4} PlotLayout(PlotEquiMap, c('lat', 'lon'), - var=malariaInd$data[1,1,,,], + var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,], nrow=1, ncol=2, lon=malariaInd$lon, lat=malariaInd$lat, filled.continents=FALSE, brks=seq(0, 12, by=1), - toptitle="Number of months per year suitable for malaria in the Iberian Peninsula", - titles=c("2010", "2011"), - title_scale=0.5, + toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula", + titles=c(as.character(year_in), as.character(year_fi)), + title_scale=0.4, coast_width=2, filled.oceans=TRUE, country.borders=TRUE, intylat=1, intxlon=1) - ``` ### 4.2 Time series -Add chunk +The annual mean number of months suitable for malaria transmission can be extracted +with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created +with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()` +function allows the user to compute a linear trend in the data. + +```{r fig.width=7, fig.height=4} +library(dplyr) +library(ggplot2) + +data.frame(year=seq(year_in, year_fi, by=1), + ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% + ggplot(aes(x=year, y=ind)) + + geom_line() + + scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) + + geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) + + labs(x="Year", y="Mean number of months suitable \n for malaria transmission") + + theme_bw() +``` -## 5. Summary statistics -Add chunk +### 4.3 Percentage change +```{r} +first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE) +last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE) +change <- round((last-first)/first * 100, 0) +change +``` ## 6. Conclusions -Add chunk +The number of months suitable for malaria transmission appear to have increased +in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with +the year 2010, there was a `r change`% increase in the number of months suitable for +malaria transmission in the region. ## References diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html index 1fafc7a..b995de2 100644 --- a/vignettes/malaria/MalariaIndicator.html +++ b/vignettes/malaria/MalariaIndicator.html @@ -12,7 +12,7 @@ - + Malaria Suitability Indicator @@ -336,7 +336,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }

Malaria Suitability Indicator

Earth Sciences department, Barcelona Supercomputing Center (BSC)

-

2022-08-29

+

2022-08-30

@@ -350,9 +350,9 @@ text-align: justify

Introduction

-

Climate sensitive infectious diseases are a subject of major concern for public health around the world in the context of global change. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic malaria in Europe since 1974, although sporadic transmission events are frequently reported in travelers coming from other endemic areas. Studies evaluating the factors that keep malaria out of the continent found that high socio-economic conditions and increased life expectancy are key (Fischer et al., 2020). On the other hand, climatic conditions are becoming more suitable for malaria transmission (Zhao et al., 2016).

-

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on the dynamic of different health processes. As part of thisinitiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) for tracking the number of months suitable of malaria transmission per year in the continent between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the parasiteof endemic circulation in the continent until the mid 1970s (Boualam et al., 2021).

-

Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural applications, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

+

Climate sensitive infectious diseases are a subject of major concern in the context of a changing climate. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic circulation of malaria in Europe, although sporadic transmission events are frequently reported in travelers coming from endemic areas. Studies evaluating drivers of malaria elimination found that high socio-economic conditions and increased life expectancy are key determinants in keeping this disease out of the continent (Fischer et al., 2020). On the other hand, the risk of malaria resurgence is present as climatic conditions become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016).

+

The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on dynamic of different health processes. As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) to track the annual number of months suitable of malaria transmission between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021).

+

Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold() function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

Load libraries

@@ -365,13 +365,13 @@ text-align: justify

1. Data

1.1 Area of interest

-

The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula (40º14´24´´N 4º14´21´´W). This area comprises the territories of Portugal, Spain, France, Andorra and Gibraltar.

+

The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.

1.2 Data extraction

-

Malaria indicator is computed with climate and environmental data. Climate variables are extracted from the ERA5-Land reanalysis dataset (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. Necessary climate variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). Environmental information is extracted from the CORINE Land Cover inventory, which classifies land into 44 classes at a 100 m resolution. These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

+

The Malaria indicator is computed with climate variables extracted from the ERA5-Land reanalysis dataset (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. These variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

The function CST_Load from the CSTools package allows the user to access the processed data and delivers the data in the form of an s2dv_cube object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit Data retrieval and storage.

CST_Load has its requirements for extracting data. First, the path to the files is introduced using whitecards delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with sdates, which takes a vector of all dates of interest. The spatial extent is inputted within lonmax, lonmin, latmax and latmin.

# Path to file locations using whitecards
@@ -379,8 +379,8 @@ text-align: justify
 
 # Temporal extent and resolution:
 # We will work with all months from 2010 to 2020
-year_in <- 2020 # initial year
-year_fi <- 2021 # last year
+year_in <- 2010 # initial year
+year_fi <- 2020 # last year
 month_in <- 1 # first month
 month_fi <- 12 # last month
 
@@ -395,8 +395,8 @@ text-align: justify
                            exp = NULL, # We only require observed data
                            obs = list(path_ERA5_CDS),
                            sdates = sdates,
-                           lonmax = 355, lonmin = 350,
-                           latmax = 45, latmin = 43,
+                           lonmax = 5, lonmin = 350,
+                           latmax = 45, latmin = 35,
                            storefreq = 'monthly',
                            leadtimemin = month_in, 
                            leadtimemax = month_fi,
@@ -414,147 +414,170 @@ text-align: justify
 #                                       leadtimemin = month_in, 
 #                                       leadtimemax = month_fi,
 #                                       output = "lonlat")
-

The call above creates a list with the three datasets. Each s2dv_cube object has the following dimensions:

+

The output object is a list with the three s2dv_cube objects with dimensions

dim(out$tas$data)
## dataset  member   sdate   ftime     lat     lon 
-##       1       1       2      12      21      51
+## 1 1 11 12 101 151

And it is possible to have a look at the data:

# A summary of the entire dataset
 summary(out$tas$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##   272.5   281.8   284.7   284.9   288.7   292.9   19680
+## 263.4 281.6 286.6 287.3 292.8 306.6 862224 
# Sneak peek of a sample of the temperature data in Jan 2010
 out$tas$data[1,1,1,1,,][15:20, 15:20]
##        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]
-## [1,]     NA     NA     NA     NA 283.70 283.39
-## [2,]     NA     NA     NA     NA 283.36 282.70
-## [3,]     NA     NA     NA 283.27 283.00 282.03
-## [4,] 282.94 282.79 282.59 282.57 282.37 281.64
-## [5,] 281.89 281.58 281.53 281.56 281.39 280.84
-## [6,] 281.18 280.96 280.88 280.82 280.56 280.11
+## [1,] 276.88 276.76 276.54 276.40 276.31 276.28 +## [2,] 276.71 276.71 276.60 276.52 276.42 276.31 +## [3,] 276.60 276.57 276.48 276.45 276.44 276.34 +## [4,] 276.68 276.54 276.31 276.21 276.27 276.36 +## [5,] 276.50 276.58 276.59 276.44 276.31 276.26 +## [6,] 275.59 275.96 276.36 276.30 276.06 275.81

2. Data transformation

2.1 Temperature and dew point temperature

-

This variables will be used to compute relative humidity, which requires them to be in Degrees Celsius (C):

+

First, it is necessary to transform units from Kelvin (K) to Degrees Celsius (C)

for(var in c('tas', 'tdps')){
   out[[var]]$data <- out[[var]]$data - 273.15
 }
 
 summary(out$tas$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##   -0.67    8.61   11.56   11.77   15.56   19.78   19680
+## -9.7 8.5 13.5 14.1 19.7 33.5 862224

2.3 Relative humidity

-

After transforming temperature (\(T\)) and dew point temperature (\(T_d\)) units from Kelvin (K) to Degrees Celsius (C), it is possible to compute the relative humidity using August-Roche-Magnus equation (Alduchov 1996):

+

Then, it is possible to calculate relative humidity (hurs) using temperature (\(T\)) and dew point temperature (\(T_d\)) with the August-Roche-Magnus equation (Alduchov 1996):

\[ -RH=\frac{100*exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} +RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})} \] Where a and b are the coefficients 17.625 and 243.04, respectively.

-
# Create a template with the same dimensions as the datasets
-out$rh <- out$tas
-
-# Compute relative humidity (RH)
-out$rh$data <- 100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
-                      exp((17.625*out$tas$data) / (243.04+out$tas$data)))
-
-summary(out$rh$data)
+
# Create a new s2dv object with relative humidity
+out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
+                                  exp((17.625*out$tas$data) / (243.04+out$tas$data))),
+                      lon=out$tas$lon,
+                      lat=out$tas$lat,
+                      Variable=list(varName="rh", level=NULL),
+                      Dates=out$tas$Dates,
+                      Datasets=out$tas$Datasets,
+                      when=Sys.time(),
+                      source_files="see source files of tas, tdps and pr")
+
+attr(out$hurs$Variable, "units") <- "%"
+attr(out$hurs$Variable, "longname") <- "near-surface relative humidity"
+
+summary(out$hurs$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##   67.55   79.30   81.90   81.88   83.97  116.08   19680
+## 19.4 55.7 68.5 65.4 77.1 95.0 862224

2.2 Precipitation

-

Precipitation is extracted from the hourly reanalaysis dataset and processes as monthly meters per second. For the computation of the malaria indicator, it is necessary to transform it into accumulated mm per month:

+

Precipitation is extracted from the hourly reanalaysis dataset and delivered in monthly meters per second. The next step is to transform it into accumulated mm per month

# To compute the accumulated precipitation per month it is necessary to multiply by:
 # - total number of seconds in one hour: 3600
 # - total number of hours in a day: 24
-# - average number of days in a month: 30.41
+# - average number of days in a month: 365.25/12=30.44
 # - change factor from meter to mm: 1000
-out$prlr$data <- out$prlr$data*3600*24*30.41*1000
+out$prlr$data <- out$prlr$data*3600*24*30.44*1000
 
 summary(out$prlr$data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##    2.22   61.84   99.72  107.85  142.38  395.53   19680
-
-
-

2.4 Land use

-

Add chunk

+## 0.0 20.1 47.5 59.9 83.9 622.2 862224

3. Compute indicator

-

The suitability for malaria transmission indicator estimated the number of months suitable for transmission of P. vivax, calculated from the following thresholds:

+

The suitability for malaria transmission indicator estimates the number of months suitable for transmission of P. vivax according to the following thresholds:

  • Accumulated monthly precipitation above 80 mm (Lyon et al., 2017),
  • Monthly temperature between 14.5ºC and 33ºC (Grover-Kopec et al., 2006), and
  • Monthly relative humidity above 60% (Grover-Kopec et al., 2006)
-

Additionally, Anopheles suitability is influenced by the surrounding conditions, so that the following land cover classes are considered highly suitable (Benali et al., 2014):

-
    -
  • Rice fields,
    • -
  • Permanently irrigated croplands, and
    • -
  • Sport and leisure facilities
    • -
-
# Create a template with the same dimensions as the datasets
-malariaSuit <- out$tas
-
-# Determine suitability
-malariaSuit$data <- ifelse(out$tas$data >= 14.5 & 
-                             out$tas$data <= 33 & 
-                             out$rh$data >= 60 & 
-                             out$prlr$data >= 80, 1, 0)
-
-# Compute number of number of months per year that are suitable
-# (i.e. suitability = 1)
-malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(malariaSuit, 
-                                                            threshold=0.5)
-
-# Sneak peek of the data
-malariaInd$data[1,1,1,,][15:20, 15:20]
+
# Create a new s2dv object with malaria suitability
+out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & 
+                                  out$tas$data <= 33 & 
+                                  out$hurs$data >= 60 & 
+                                  out$prlr$data >= 80, 1, 0),
+                    lon=out$tas$lon,
+                    lat=out$tas$lat,
+                    Variable=list(varName="malar_suit", level=NULL),
+                    Dates=out$tas$Dates,
+                    Datasets=out$tas$Datasets,
+                    when=Sys.time(),
+                    source_files="see source files of tas, tdps and pr")
+
+attr(out$malariaSuit$Variable, "units") <- "none"
+attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission"
+
+# Compute number of number of months per year that are suitable
+# (i.e. suitability = 1)
+malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, 
+                                                            threshold=0.5)
+
+# Sneak peek of the data
+malariaInd$data[1,1,1,,][15:20, 15:20]
##      [,1] [,2] [,3] [,4] [,5] [,6]
-## [1,]   NA   NA   NA   NA    2    2
-## [2,]   NA   NA   NA   NA    2    2
-## [3,]   NA   NA   NA    2    1    2
-## [4,]    1    1    1    1    1    2
-## [5,]    1    1    1    1    1    1
-## [6,]    0    1    1    1    1    1
+## [1,] 0 0 0 0 0 1 +## [2,] 0 0 0 0 0 0 +## [3,] 0 0 0 0 0 0 +## [4,] 0 0 0 0 0 0 +## [5,] 1 0 0 0 0 0 +## [6,] 1 1 1 1 1 0 +
summary(malariaInd$data)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
+##    0.00    0.00    0.00    0.63    1.00    6.00   71852
-
-

4. Visualization

+
+

4. Results

4.1. Map

Using the function PlotEquiMap() from the s2dv package, it is possible to have a visual inspection of the number of suitable months in January 2010.

-
PlotLayout(PlotEquiMap, c('lat', 'lon'),
-           var=malariaInd$data[1,1,,,],
-           nrow=1, ncol=2,
-           lon=malariaInd$lon,
-           lat=malariaInd$lat,
-           filled.continents=FALSE,
-           brks=seq(0, 12, by=1),
-           toptitle="Number of months per year suitable for malaria in the Iberian Peninsula",
-           titles=c("2010", "2011"),
-           title_scale=0.5,
-           coast_width=2,
-           filled.oceans=TRUE,
-           country.borders=TRUE,
-           intylat=1,
-           intxlon=1)
-

+
PlotLayout(PlotEquiMap, c('lat', 'lon'),
+           var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
+           nrow=1, ncol=2,
+           lon=malariaInd$lon,
+           lat=malariaInd$lat,
+           filled.continents=FALSE,
+           brks=seq(0, 12, by=1),
+           toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula",
+           titles=c(as.character(year_in), as.character(year_fi)),
+           title_scale=0.4,
+           coast_width=2,
+           filled.oceans=TRUE,
+           country.borders=TRUE,
+           intylat=1,
+           intxlon=1)
+

4.2 Time series

-

Add chunk

+

The annual mean number of months suitable for malaria transmission can be extracted with the MeanDims() function from the s2dv package. Then, a plot can be created with functions from the dplyr and ggplot2 packages. Additionally, the ggplot() function allows the user to compute a linear trend in the data.

+
library(dplyr)
+library(ggplot2)
+
+data.frame(year=seq(year_in, year_fi, by=1),
+           ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
+  ggplot(aes(x=year, y=ind)) +
+  geom_line() +
+  scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) +
+  geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) +
+  labs(x="Year", y="Mean number of months suitable \n for malaria transmission") +
+  theme_bw()
+

+
+

4.3 Percentage change

+
first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE)
+last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE)
+change <- round((last-first)/first * 100, 0)
+change
+
## [1] 47
-
-

5. Summary statistics

-

Add chunk

6. Conclusions

-

Add chunk

+

The number of months suitable for malaria transmission appear to have increased in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with the year 2010, there was a 47% increase in the number of months suitable for malaria transmission in the region.

References

diff --git a/vignettes/malaria/call_data.R b/vignettes/malaria/call_data.R deleted file mode 100644 index 57d219b..0000000 --- a/vignettes/malaria/call_data.R +++ /dev/null @@ -1,90 +0,0 @@ -# Learning how to use StartR -#setwd("/home/Earth/mlotto/lcd_projects/csindicators/vignettes/malaria") -# source("call_data.R") - -source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") - -# load several months and bind them to the same R object: -# Dimensions: -# - var= number of variables (in general just one) -# - sdate= number of years -# - time= number of months -# - lat and lon= coordinates - -# example of use -# -path_ERA5tas_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc') - - year_in <- 2010 - year_fi <- 2011 - month_in <- 1 - month_fi <- 12 - - sdates <- paste0(year_in:year_fi, '0101') - - loaded_obs <- CST_Load_s2dv(var = 'tas', - exp = NULL, - obs = list(path_ERA5tas_CDS), - sdates = sdates, - lonmax = 353, lonmin = 352.25, - latmax = 41.75, latmin = 41, - storefreq = 'monthly', - leadtimemin = month_in, leadtimemax = month_fi, - output = "lonlat") - - - loaded_obs2 <- CST_Load_s2dv(var = 'prlr', - exp = NULL, - obs = list(path_ERA5tas_CDS), - sdates = sdates, - lonmax = 353, lonmin = 352.25, - latmax = 41.75, latmin = 41, - storefreq = 'monthly', - leadtimemin = month_in, leadtimemax = month_fi, - output = "lonlat") - - tas3 <- tas2 <- tas1 <- tas <- loaded_obs - pr3 <- pr2 <- pr1 <- pr <- loaded_obs2 - - tas1$data <- tas$data - 273.15 - pr1$data <- pr$data * 30410 - - tas3$data <- ifelse(tas1$data >= 4 & pr1$data >= 0.0012, 1, 0) - - test1$data <- loaded_obs$data - 273.15 - test2$data <- ifelse(test1$data >= 5, NA, test1$data) - test2$data <- ifelse(test1$data >= 4 & test1$data <=5, 1, 0) - test2$data <- ifelse(test1$data >= 4 & test1$data <=5, 1, 0) - - - - - summary(test1$data) - - - - test2$data <- test$data+test$data - test3$data <- test$data*2 - - # change the unit of temperature from °C to K - - tas_exp$data <- tas_exp$data - 273.15 - tas_obs$data <- tas_obs$data - 273.15 - - - -dim(loaded_obs$data) -#dataset member sdate ftime lat lon -# 1 1 4 12 8 8 - - - - -year_in <- 2013 -year_fi <- 2014 -lat_min <- 41 -lat_max <- 41.75 -lon_min <- 352.25 -lon_max <- 353 -data <- load_monthly_data_all_variables_and_months(year_in, year_fi, lat_min, lat_max, lon_min, lon_max) - diff --git a/vignettes/malaria/ind_computation.R b/vignettes/malaria/ind_computation.R deleted file mode 100644 index 8a7658f..0000000 --- a/vignettes/malaria/ind_computation.R +++ /dev/null @@ -1,134 +0,0 @@ -########### -# 1. Extract temperature data for a period of 10 years and for a specific region of the Iberian Peninsula -# 2. Convert temperature from Kelvin to Degrees -# 3. Extract dew point temperature for the same period and region -# 4. Convert temperature from Kelvin to Degrees -# 5. Compute relative humidity -# 6. Extract precipitation for the same period and region -# 7. Convert m to mm -# 8. Create the binary indicator -# 9. Use totaltimeexceedingthreshold to create summary value per year -######### - -source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R") -source("plot_map.R") - -path_ERA5tas_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc') - -year_in <- 2020 -year_fi <- 2021 -month_in <- 1 -month_fi <- 12 - -sdates <- paste0(year_in:year_fi, '0101') - -### TEMPERATURE (TAS) -# 1. Extract sample temperature -tas_obs <- CST_Load_s2dv(var = 'tas', - exp = NULL, - obs = list(path_ERA5tas_CDS), - sdates = sdates, - lonmax = 355, lonmin = 350, - latmax = 45, latmin = 43, - storefreq = 'monthly', - leadtimemin = month_in, - leadtimemax = month_fi, - output = "lonlat") - -# 2. Convert temperature from Kelvin to Degrees -tas_obs$data <- tas_obs$data-273.15 - -### DEW POINT TEMPERATURE (TDPS) -# 3. Extract sample dew point temperature -tdps_obs <- CST_Load_s2dv(var = 'tdps', - exp = NULL, - obs = list(path_ERA5tas_CDS), - sdates = sdates, - lonmax = 355, lonmin = 350, - latmax = 45, latmin = 43, - storefreq = 'monthly', - leadtimemin = month_in, - leadtimemax = month_fi, - output = "lonlat") - - -# 4. Convert from Kelvin to Degrees -tdps_obs$data <- tdps_obs$data-273.15 - -# 5. Compute rel humidity -rel_hum <- tdps_obs -rel_hum$data <- 100*(exp((17.625*tdps_obs$data)/(243.04+tdps_obs$data)) / exp((17.625*tas_obs$data) / (243.04+tas_obs$data))) - -#### PRECIPITATION (PRLR) -# 6. Extract sample precipitation -pr_obs <- CST_Load_s2dv(var = 'prlr', - exp = NULL, - obs = list(path_ERA5tas_CDS), - sdates = sdates, - lonmax = 355, lonmin = 350, - latmax = 45, latmin = 43, - storefreq = 'monthly', - leadtimemin = month_in, - leadtimemax = month_fi, - output = "lonlat") - -# 7. Convert m/month to mm/moth -# Precipitation is downloaded and transformed as a flux of meters per second per -# month, to transform it to mm/month it is necessary to multiply it by: -# - total number of seconds in one hour: 3600 -# - total number of hours in a day: 24 -# - average number of days in a month: 30.41 -# - change from meter to mm: 1000 -pr_obs$data <- pr_obs$data*3600*24*30.41*1000 - -#### INDICATOR -# 8. Create the binary indicator -# i) Create individual suitability per variable for checking purposes -t1 <- r1 <- p1 <- pr_obs -t1$data <- ifelse(tas_obs$data >= 14.5 & tas_obs$data <= 33,1,0) -# tas_obs$data[1,1,1,6,,][15:19, 15:20] -# t1$data[1,1,1,6,,][15:19, 15:20] - -r1$data <- ifelse(rel_hum$data >= 60,1,0) -# rel_hum$data[1,1,1,6,,][15:19, 15:20] -# r1$data[1,1,1,6,,][15:19, 15:20] - -p1$data <- ifelse(pr_obs$data >= 80,1,0) -# pr_obs$data[1,1,1,6,,][15:19, 15:20] -# p1$data[1,1,1,6,,][15:19, 15:20] - -# ii) Create climate suitability -suit_ind <- pr_obs -suit_ind$data <- ifelse(tas_obs$data >= 14.5 & tas_obs$data <= 33 & rel_hum$data >= 60 & pr_obs$data >= 80,1,0) -# suit_ind$data[1,1,1,6,,][15:19, 15:20] - -# 9. Use CSIndicators::TotalTimeExceedingThreshold() to compute indicator -test <- CSIndicators::CST_TotalTimeExceedingThreshold(suit_ind, threshold=0.5)#, time_dim="sdate") - -dim(test$data) -test$data[1,1,2,,][15:19, 15:20] - - -############## -# Test raster file -library(raster) - - -path.c <- "/esarchive/recon/ecmwf/era5land/monthly_mean/tas_f1h/tas_202108.nc" -templ <- raster(path.c) - -e <- raster::extent(350, 355, 43, 45) -templ <- crop(templ, e) -templ1 <- projectRaster(templ, crs = crs(test), res = res(test)) - -path <- "/esarchive/recon/ecmwf/clc/original_files/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif" -test <- raster(path) -r <- crop(test, extent(templ1)) - -test1 <- as.matrix(r) -test2 <- s2dv_cube(test1) - -test1 <- s2dv_cube(array(r, dim=c(dat=1, sdate=1, ftime=1))) - - - diff --git a/vignettes/malaria/make_figs.R b/vignettes/malaria/make_figs.R deleted file mode 100644 index f8ef703..0000000 --- a/vignettes/malaria/make_figs.R +++ /dev/null @@ -1,3 +0,0 @@ -# Creaing figures for CSIndicators vignette about the malaria indicator - -source("plot_map.R") \ No newline at end of file -- GitLab From 6c6b2b0436ef22e672ac0e0a6e2d3cc9156bb5a0 Mon Sep 17 00:00:00 2001 From: mlotto Date: Tue, 30 Aug 2022 18:11:46 +0200 Subject: [PATCH 08/29] fix typos --- vignettes/malaria/MalariaIndicator.Rmd | 5 +++-- vignettes/malaria/MalariaIndicator.html | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/malaria/MalariaIndicator.Rmd index 11b5b71..2194069 100644 --- a/vignettes/malaria/MalariaIndicator.Rmd +++ b/vignettes/malaria/MalariaIndicator.Rmd @@ -5,7 +5,7 @@ date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteEngine{knitr::knitr} - %\VignetteIndexEntry{Agricultural Indicators} + %\VignetteIndexEntry{Malaria Indicator} %\usepackage[utf8]{inputenc} --- @@ -255,7 +255,8 @@ summary(malariaInd$data) ## 4. Results ### 4.1. Map Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to -have a visual inspection of the number of suitable months in January 2010. +have a visual inspection of the number of suitable months of a particular year. +In this, the first and last years of the time series. ```{r echo=FALSE} # Source master branch for PlotLayout() diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html index b995de2..9ff04ad 100644 --- a/vignettes/malaria/MalariaIndicator.html +++ b/vignettes/malaria/MalariaIndicator.html @@ -532,7 +532,7 @@ RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}

4. Results

4.1. Map

-

Using the function PlotEquiMap() from the s2dv package, it is possible to have a visual inspection of the number of suitable months in January 2010.

+

Using the function PlotEquiMap() from the s2dv package, it is possible to have a visual inspection of the number of suitable months of a particular year. In this, the first and last years of the time series.

PlotLayout(PlotEquiMap, c('lat', 'lon'),
            var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
            nrow=1, ncol=2,
-- 
GitLab


From 8e71a235e0355e93c4dbad64a6a48621530fc03a Mon Sep 17 00:00:00 2001
From: mlotto 
Date: Wed, 31 Aug 2022 11:46:57 +0200
Subject: [PATCH 09/29] reorganize files

---
 vignettes/{malaria => }/MalariaIndicator.Rmd |   0
 vignettes/malaria/MalariaIndicator.html      | 613 -------------------
 vignettes/malaria/malaria.Rproj              |  13 -
 3 files changed, 626 deletions(-)
 rename vignettes/{malaria => }/MalariaIndicator.Rmd (100%)
 delete mode 100644 vignettes/malaria/MalariaIndicator.html
 delete mode 100644 vignettes/malaria/malaria.Rproj

diff --git a/vignettes/malaria/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
similarity index 100%
rename from vignettes/malaria/MalariaIndicator.Rmd
rename to vignettes/MalariaIndicator.Rmd
diff --git a/vignettes/malaria/MalariaIndicator.html b/vignettes/malaria/MalariaIndicator.html
deleted file mode 100644
index 9ff04ad..0000000
--- a/vignettes/malaria/MalariaIndicator.html
+++ /dev/null
@@ -1,613 +0,0 @@
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-Malaria Suitability Indicator
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Malaria Suitability Indicator

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Earth Sciences department, Barcelona Supercomputing Center (BSC)

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2022-08-30

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-
Climate Suitability for Malaria Transmission

-

-

-
Introduction

-
Climate sensitive infectious diseases are a subject of major concern in the context of a changing climate. Malaria is an infectious disease caused by parasites of the genus Plasmodium and transmitted by Anopheles mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic circulation of malaria in Europe, although sporadic transmission events are frequently reported in travelers coming from endemic areas. Studies evaluating drivers of malaria elimination found that high socio-economic conditions and increased life expectancy are key determinants in keeping this disease out of the continent (Fischer et al., 2020). On the other hand, the risk of malaria resurgence is present as climatic conditions become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016).

-
The Lancet Countdown in Europe collaboration was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on dynamic of different health processes. As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the Global Lancet Countdown Collaboration (Romanello et al., 2021) to track the annual number of months suitable of malaria transmission between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of Anopheles mosquitoes and Plasmodium vivax, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021).

-
Although the CSIndicators was initially developed for providing a set of tools useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the TotalTimeExceedingThreshold() function. The output will be an multidimensional array containing the number of months per year suitable for malaria transmission.

-

-

-
Load libraries

-
In addition to the CSIndicators package, the CSTools and s2dv packages will be used for data extraction and visualization.

-
library(CSTools)
-library(CSIndicators)
-library(s2dv)

-

-

-
1. Data

-

-
1.1 Area of interest

-
The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.

-
-

-

-
1.2 Data extraction

-
The Malaria indicator is computed with climate variables extracted from the ERA5-Land reanalysis dataset (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. These variables are 2 meter temperature (tas), 2 meter dew point temperature (tdps) and total precipitation (prlr). These data are produced by the Copernicus service and extracted and reprocessed by the Earth Sciences department from the Barcelona Supercomputing Center.

-
The function CST_Load from the CSTools package allows the user to access the processed data and delivers the data in the form of an s2dv_cube object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit Data retrieval and storage.

-
CST_Load has its requirements for extracting data. First, the path to the files is introduced using whitecards delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with sdates, which takes a vector of all dates of interest. The spatial extent is inputted within lonmax, lonmin, latmax and latmin.

-
# Path to file locations using whitecards
-path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
-
-# Temporal extent and resolution:
-# We will work with all months from 2010 to 2020
-year_in <- 2010 # initial year
-year_fi <- 2020 # last year
-month_in <- 1 # first month
-month_fi <- 12 # last month
-
-sdates <- paste0(year_in:year_fi, '0101')
-
-# Extract data
-vars <- c('tas', 'tdps', 'prlr')
-
-out <- NULL
-for(var in vars){
-  out[[var]] <- CST_Load_s2dv(var = var,
-                           exp = NULL, # We only require observed data
-                           obs = list(path_ERA5_CDS),
-                           sdates = sdates,
-                           lonmax = 5, lonmin = 350,
-                           latmax = 45, latmin = 35,
-                           storefreq = 'monthly',
-                           leadtimemin = month_in, 
-                           leadtimemax = month_fi,
-                           output = "lonlat")  
-}
-
-# library(zeallot)
-# c(tas, tdps, prlr) %<-% CST_Load_s2dv(var = c('tas', 'tdps', 'prlr'),
-#                                       exp = NULL, # We only require observed data
-#                                       obs = list(path_ERA5_CDS),
-#                                       sdates = sdates,
-#                                       lonmax = 355, lonmin = 350,
-#                                       latmax = 45, latmin = 43,
-#                                       storefreq = 'monthly',
-#                                       leadtimemin = month_in, 
-#                                       leadtimemax = month_fi,
-#                                       output = "lonlat")  

-
The output object is a list with the three s2dv_cube objects with dimensions

-
dim(out$tas$data)

-
## dataset  member   sdate   ftime     lat     lon 
-##       1       1      11      12     101     151

-
And it is possible to have a look at the data:

-
# A summary of the entire dataset
-summary(out$tas$data)

-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##   263.4   281.6   286.6   287.3   292.8   306.6  862224

-
# Sneak peek of a sample of the temperature data in Jan 2010
-out$tas$data[1,1,1,1,,][15:20, 15:20]

-
##        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]
-## [1,] 276.88 276.76 276.54 276.40 276.31 276.28
-## [2,] 276.71 276.71 276.60 276.52 276.42 276.31
-## [3,] 276.60 276.57 276.48 276.45 276.44 276.34
-## [4,] 276.68 276.54 276.31 276.21 276.27 276.36
-## [5,] 276.50 276.58 276.59 276.44 276.31 276.26
-## [6,] 275.59 275.96 276.36 276.30 276.06 275.81

-

-

-

-
2. Data transformation

-

-
2.1 Temperature and dew point temperature

-
First, it is necessary to transform units from Kelvin (K) to Degrees Celsius (C)

-
for(var in c('tas', 'tdps')){
-  out[[var]]$data <- out[[var]]$data - 273.15
-}
-
-summary(out$tas$data)

-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##    -9.7     8.5    13.5    14.1    19.7    33.5  862224

-

-

-
2.3 Relative humidity

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Then, it is possible to calculate relative humidity (hurs) using temperature (\(T\)) and dew point temperature (\(T_d\)) with the August-Roche-Magnus equation (Alduchov 1996):

-
\[
-RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}
-\] Where a and b are the coefficients 17.625 and 243.04, respectively.

-
# Create a new s2dv object with relative humidity
-out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
-                                  exp((17.625*out$tas$data) / (243.04+out$tas$data))),
-                      lon=out$tas$lon,
-                      lat=out$tas$lat,
-                      Variable=list(varName="rh", level=NULL),
-                      Dates=out$tas$Dates,
-                      Datasets=out$tas$Datasets,
-                      when=Sys.time(),
-                      source_files="see source files of tas, tdps and pr")
-
-attr(out$hurs$Variable, "units") <- "%"
-attr(out$hurs$Variable, "longname") <- "near-surface relative humidity"
-
-summary(out$hurs$data)

-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##    19.4    55.7    68.5    65.4    77.1    95.0  862224

-

-

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2.2 Precipitation

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Precipitation is extracted from the hourly reanalaysis dataset and delivered in monthly meters per second. The next step is to transform it into accumulated mm per month

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# To compute the accumulated precipitation per month it is necessary to multiply by:
-# - total number of seconds in one hour: 3600
-# - total number of hours in a day: 24
-# - average number of days in a month: 365.25/12=30.44
-# - change factor from meter to mm: 1000
-out$prlr$data <- out$prlr$data*3600*24*30.44*1000
-
-summary(out$prlr$data)

-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##     0.0    20.1    47.5    59.9    83.9   622.2  862224

-

-

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3. Compute indicator

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The suitability for malaria transmission indicator estimates the number of months suitable for transmission of P. vivax according to the following thresholds:

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  • Accumulated monthly precipitation above 80 mm (Lyon et al., 2017),
    • 
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  • Monthly temperature between 14.5ºC and 33ºC (Grover-Kopec et al., 2006), and
    • 
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  • Monthly relative humidity above 60% (Grover-Kopec et al., 2006)
    • 
-

-
# Create a new s2dv object with malaria suitability
-out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & 
-                                  out$tas$data <= 33 & 
-                                  out$hurs$data >= 60 & 
-                                  out$prlr$data >= 80, 1, 0),
-                    lon=out$tas$lon,
-                    lat=out$tas$lat,
-                    Variable=list(varName="malar_suit", level=NULL),
-                    Dates=out$tas$Dates,
-                    Datasets=out$tas$Datasets,
-                    when=Sys.time(),
-                    source_files="see source files of tas, tdps and pr")
-
-attr(out$malariaSuit$Variable, "units") <- "none"
-attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission"
-
-# Compute number of number of months per year that are suitable
-# (i.e. suitability = 1)
-malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, 
-                                                            threshold=0.5)
-
-# Sneak peek of the data
-malariaInd$data[1,1,1,,][15:20, 15:20]

-
##      [,1] [,2] [,3] [,4] [,5] [,6]
-## [1,]    0    0    0    0    0    1
-## [2,]    0    0    0    0    0    0
-## [3,]    0    0    0    0    0    0
-## [4,]    0    0    0    0    0    0
-## [5,]    1    0    0    0    0    0
-## [6,]    1    1    1    1    1    0

-
summary(malariaInd$data)

-
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-##    0.00    0.00    0.00    0.63    1.00    6.00   71852

-

-

-
4. Results

-

-
4.1. Map

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Using the function PlotEquiMap() from the s2dv package, it is possible to have a visual inspection of the number of suitable months of a particular year. In this, the first and last years of the time series.

-
PlotLayout(PlotEquiMap, c('lat', 'lon'),
-           var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
-           nrow=1, ncol=2,
-           lon=malariaInd$lon,
-           lat=malariaInd$lat,
-           filled.continents=FALSE,
-           brks=seq(0, 12, by=1),
-           toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula",
-           titles=c(as.character(year_in), as.character(year_fi)),
-           title_scale=0.4,
-           coast_width=2,
-           filled.oceans=TRUE,
-           country.borders=TRUE,
-           intylat=1,
-           intxlon=1)

-

-

-

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4.2 Time series

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The annual mean number of months suitable for malaria transmission can be extracted with the MeanDims() function from the s2dv package. Then, a plot can be created with functions from the dplyr and ggplot2 packages. Additionally, the ggplot() function allows the user to compute a linear trend in the data.

-
library(dplyr)
-library(ggplot2)
-
-data.frame(year=seq(year_in, year_fi, by=1),
-           ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
-  ggplot(aes(x=year, y=ind)) +
-  geom_line() +
-  scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) +
-  geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) +
-  labs(x="Year", y="Mean number of months suitable \n for malaria transmission") +
-  theme_bw()

-

-

-

-
4.3 Percentage change

-
first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE)
-last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE)
-change <- round((last-first)/first * 100, 0)
-change

-
## [1] 47

-

-

-

-
6. Conclusions

-
The number of months suitable for malaria transmission appear to have increased in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with the year 2010, there was a 47% increase in the number of months suitable for malaria transmission in the region.

-

-

-
References

-
    
-
  • Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. J Appl Meteorl Climatol. 1996; 35(4),601-609. URL
    • 
-
  • Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. Remote Sens Environ. 2014;145:116-130. DOI:10.1016/J.RSE.2014.01.014
    • 
-
  • Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. Front Med. 2021; 8:876. DOI:10.3389/FMED.2021.691095/BIBTEX
    • 
-
  • Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. Travel Med Infect Dis. 2020;36:101815. DOI:10.1016/J.TMAID.2020.101815
    • 
-
  • Grover-Kopec EK, Blumenthal MB, Ceccato P, Dinku T, Omumbo JA, Connor SJ. Web-based climate information resources for malaria control in Africa. Malar J. 2006;5(1):1-9. DOI:10.1186/1475-2875-5-38/FIGURES/5
    • 
-
  • Lyon B, Dinku T, Raman A, Thomson MC. Temperature suitability for malaria climbing the Ethiopian Highlands. Environ Res Lett. 2017;12(6):064015. DOI:10.1088/1748-9326/AA64E6
    • 
-
  • Muñoz-Sabater J, Dutra E, Agustí-Panareda A, et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data. 2021;13(9):4349-4383. DOI:10.5194/ESSD-13-4349-2021
    • 
-
  • Romanello M, McGushin A, Di Napoli C, et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet. 2021;398(10311):1619-1662. DOI:https://doi.org/10.1016/S0140-6736(21)01787-6
    • 
-
  • Zhao X, Smith DL, Tatem AJ. Exploring the spatiotemporal drivers of malaria elimination in Europe. Malar J. 2016;15(1):1-13. DOI:10.1186/S12936-016-1175-Z/TABLES/5
    • 
-

-

-
-
-
-
-
-
-
-
-
-
-
diff --git a/vignettes/malaria/malaria.Rproj b/vignettes/malaria/malaria.Rproj
deleted file mode 100644
index 8e3c2eb..0000000
--- a/vignettes/malaria/malaria.Rproj
+++ /dev/null
@@ -1,13 +0,0 @@
-Version: 1.0
-
-RestoreWorkspace: Default
-SaveWorkspace: Default
-AlwaysSaveHistory: Default
-
-EnableCodeIndexing: Yes
-UseSpacesForTab: Yes
-NumSpacesForTab: 2
-Encoding: UTF-8
-
-RnwWeave: Sweave
-LaTeX: pdfLaTeX
-- 
GitLab


From 31c6ea924a1a69153ae4b0728a4a3e4b93f2b7aa Mon Sep 17 00:00:00 2001
From: mlotto 
Date: Wed, 31 Aug 2022 12:04:43 +0200
Subject: [PATCH 10/29] fix typo

---
 vignettes/MalariaIndicator.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index 2194069..534a71d 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -323,7 +323,7 @@ change
 The number of months suitable for malaria transmission appear to have increased 
 in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with
 the year 2010, there was a `r change`% increase in the number of months suitable for
-malaria transmission in the region.
+malaria transmission.
 
 ## References
 
-- 
GitLab


From 7b13676b67a0d71b714002d4f977e5d89c1dd41d Mon Sep 17 00:00:00 2001
From: Eva Rifa 
Date: Wed, 31 Aug 2022 13:01:42 +0200
Subject: [PATCH 11/29] Correct format of the Malaria vignette

---
 vignettes/MalariaIndicator.Rmd | 222 +++++++++++++--------------------
 1 file changed, 84 insertions(+), 138 deletions(-)

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index 2194069..34f6729 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -9,12 +9,6 @@ vignette: >
   %\usepackage[utf8]{inputenc}
 ---
 
-
-
 ```{r echo=FALSE}
 knitr::opts_chunk$set(warning=FALSE, message=FALSE)
 ```
@@ -25,42 +19,18 @@ Climate Suitability for Malaria Transmission
 
 ## Introduction
 
-Climate sensitive infectious diseases are a subject of major concern in the context 
-of a changing climate. Malaria is an infectious 
-disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ 
-mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete 
-elimination of endemic  circulation of malaria in Europe, although sporadic transmission 
-events are frequently reported in travelers coming from endemic areas. Studies 
-evaluating drivers of malaria elimination found that high socio-economic 
-conditions and increased life expectancy are key determinants in keeping this disease
-out of the continent  (Fischer et al., 2020). On the 
-other hand, the risk of malaria resurgence is present as climatic conditions 
-become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
-
-The [Lancet Countdown in Europe collaboration]("https://www.lancetcountdown.org/europe/") 
-was launched in 2021 with the purpose of using a variety of indicators to monitor 
-trends in the impact of climate on dynamic of different health processes. As 
-part of this initiative, the Climate Suitability for Malaria Transmission indicator 
-was adapted from the [Global Lancet Countdown Collaboration]("https://www.lancetcountdown.org/") 
-(Romanello et al., 2021) 
-to track the annual number of months suitable of malaria transmission between 1950 
-and 2021.This indicator is a threshold based model that 
-overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
-_Plasmodium vivax_, which was the endemic parasite in the continent 
-until the mid 1970s (Boualam et al., 2021).
-
-Although the `CSIndicators` was initially developed for providing a set of tools
-useful in the context of agricultural practices, it is also possible to expand
-its applications to the computation of other type of indicators. In this vignette,
-the Climate Suitability  for Malaria indicator will be computed using  the `TotalTimeExceedingThreshold()` 
-function. The output will be an multidimensional array containing the number of 
+Climate sensitive infectious diseases are a subject of major concern in the context of a changing climate. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic circulation of malaria in Europe, although sporadic transmission events are frequently reported in travelers coming from endemic areas. Studies evaluating drivers of malaria elimination found that high socio-economic conditions and increased life expectancy are key determinants in keeping this disease out of the continent (Fischer et al., 2020). On the other hand, the risk of malaria resurgence is present as climatic conditions become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
+
+The [Lancet Countdown in Europe collaboration](https://www.lancetcountdown.org/europe/) was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on dynamic of different health processes. As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the [Global Lancet Countdown Collaboration](https://www.lancetcountdown.org/) (Romanello et al., 2021) to track the annual number of months suitable of malaria transmission between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
+_Plasmodium vivax_, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021).
+
+Although the `CSIndicators` was initially developed for providing a set of tools useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold()` function. The output will be an multidimensional array containing the number of 
 months per year suitable for malaria transmission.
 
 
 ## Load libraries
 
-In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will
-be used for data extraction and visualization.
+In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will be used for data extraction and visualization.
 
 ```{r eval=FALSE}
 library(CSTools)
@@ -70,7 +40,7 @@ library(s2dv)
 
 
 ```{r echo=FALSE}
-#Temporary links to functions
+# Temporary links to functions
 library(CSIndicators)
 source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R")
 ```
@@ -78,8 +48,7 @@ source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from
 
 ## 1. Data
 ### 1.1 Area of interest
-The number of months suitable for malaria transmission between 2010 and 2020 will
-be computed for the Iberian Peninsula.
+The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.
 
 ```{r echo=FALSE}
 knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
@@ -87,27 +56,15 @@ knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/m
 
 ### 1.2 Data extraction
 The Malaria indicator is computed with climate variables extracted from the 
-[ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950
-and present day, at a 9km resolution. These variables are 2 meter 
-temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation
- (`prlr`). These data are produced 
-by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** 
-from the **Barcelona Supercomputing Center**.
-
-The function `CST_Load` from the `CSTools` package allows the user to access the
-processed data and delivers the data in the form of an `s2dv_cube` object. These
-multidimensional objects store data and metadata in several elements. For further 
-information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
-
-`CST_Load` has its requirements for extracting data. First, the path to the files 
-is introduced using *whitecards* delimited by dollar signs. Second, the temporal 
-extent and resolution of the output con be provided with `sdates`, which takes a 
-vector of all dates of interest. The spatial extent is inputted within `lonmax`, 
-`lonmin`, `latmax` and `latmin.`
+[ERA5-Land reanalysis dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. These variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation (`prlr`). These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**.
+
+The function `CST_Load` from the `CSTools` package allows the user to access the processed data and delivers the data in the form of an `s2dv_cube` object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
+
+`CST_Load` has its requirements for extracting data. First, the path to the files is introduced using *whitecards* delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with `sdates`, which takes a vector of all dates of interest. The spatial extent is inputted within `lonmax`, `lonmin`, `latmax` and `latmin.`
 
 ```{r}
 # Path to file locations using whitecards
-path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
+path_ERA5_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
 
 # Temporal extent and resolution:
 # We will work with all months from 2010 to 2020
@@ -124,15 +81,15 @@ vars <- c('tas', 'tdps', 'prlr')
 out <- NULL
 for(var in vars){
   out[[var]] <- CST_Load_s2dv(var = var,
-                           exp = NULL, # We only require observed data
-                           obs = list(path_ERA5_CDS),
-                           sdates = sdates,
-                           lonmax = 5, lonmin = 350,
-                           latmax = 45, latmin = 35,
-                           storefreq = 'monthly',
-                           leadtimemin = month_in, 
-                           leadtimemax = month_fi,
-                           output = "lonlat")  
+                              exp = NULL, # We only require observed data
+                              obs = list(path_ERA5_CDS),
+                              sdates = sdates,
+                              lonmax = 5, lonmin = 350,
+                              latmax = 45, latmin = 35,
+                              storefreq = 'monthly',
+                              leadtimemin = month_in, 
+                              leadtimemax = month_fi,
+                              output = "lonlat")  
 }
 
 # library(zeallot)
@@ -160,7 +117,7 @@ summary(out$tas$data)
 
 ```{r}
 # Sneak peek of a sample of the temperature data in Jan 2010
-out$tas$data[1,1,1,1,,][15:20, 15:20]
+out$tas$data[1, 1, 1, 1, , ][15:20, 15:20]
 ```
 
 ## 2. Data transformation
@@ -175,25 +132,25 @@ summary(out$tas$data)
 ```
 
 ### 2.3 Relative humidity
-Then, it is possible to calculate relative humidity (hurs) using temperature ($T$) 
-and dew point temperature ($T_d$) with the August-Roche-Magnus equation (Alduchov 1996):
+Then, it is possible to calculate relative humidity (hurs) using temperature ($`T`$) and dew point temperature ($`T_d`$) with the August-Roche-Magnus equation (Alduchov 1996):
 
-$$
+```math
 RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}
-$$
-Where _a_ and _b_ are the coefficients 17.625 and 243.04, respectively.
+```
+
+Where ($`a`$) and ($`b`$) are the coefficients 17.625 and 243.04, respectively.
 
 ```{r}
 # Create a new s2dv object with relative humidity
-out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
-                                  exp((17.625*out$tas$data) / (243.04+out$tas$data))),
-                      lon=out$tas$lon,
-                      lat=out$tas$lat,
-                      Variable=list(varName="rh", level=NULL),
-                      Dates=out$tas$Dates,
-                      Datasets=out$tas$Datasets,
-                      when=Sys.time(),
-                      source_files="see source files of tas, tdps and pr")
+out$hurs <- s2dv_cube(data = 100*(exp((17.625 * out$tdps$data) / (243.04 + out$tdps$data)) / 
+                                  exp((17.625 * out$tas$data) / (243.04 + out$tas$data))),
+                      lon = out$tas$lon,
+                      lat = out$tas$lat,
+                      Variable = list(varName = "rh", level =NULL),
+                      Dates = out$tas$Dates,
+                      Datasets = out$tas$Datasets,
+                      when = Sys.time(),
+                      source_files = "see source files of tas, tdps and pr")
 
 attr(out$hurs$Variable, "units") <- "%"
 attr(out$hurs$Variable, "longname") <- "near-surface relative humidity"
@@ -202,16 +159,14 @@ summary(out$hurs$data)
 ```
 
 ### 2.2 Precipitation
-Precipitation is extracted from the hourly reanalaysis dataset and delivered in
-monthly meters per second. The next step is to transform it into accumulated mm 
-per month
+Precipitation is extracted from the hourly reanalaysis dataset and delivered in monthly meters per second. The next step is to transform it into accumulated mm per month
 ```{r}
 # To compute the accumulated precipitation per month it is necessary to multiply by:
 # - total number of seconds in one hour: 3600
 # - total number of hours in a day: 24
 # - average number of days in a month: 365.25/12=30.44
 # - change factor from meter to mm: 1000
-out$prlr$data <- out$prlr$data*3600*24*30.44*1000
+out$prlr$data <- out$prlr$data * 3600 * 24 * 30.44 * 1000
 
 summary(out$prlr$data)
 ```
@@ -226,37 +181,34 @@ suitable for transmission of _P. vivax_ according to the following thresholds:
 
 ```{r}
 # Create a new s2dv object with malaria suitability
-out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & 
-                                  out$tas$data <= 33 & 
-                                  out$hurs$data >= 60 & 
-                                  out$prlr$data >= 80, 1, 0),
-                    lon=out$tas$lon,
-                    lat=out$tas$lat,
-                    Variable=list(varName="malar_suit", level=NULL),
-                    Dates=out$tas$Dates,
-                    Datasets=out$tas$Datasets,
-                    when=Sys.time(),
-                    source_files="see source files of tas, tdps and pr")
+out$malariaSuit <- s2dv_cube(data = ifelse(out$tas$data >= 14.5 & 
+                                    out$tas$data <= 33 & 
+                                    out$hurs$data >= 60 & 
+                                    out$prlr$data >= 80, 1, 0),
+                             lon = out$tas$lon,
+                             lat = out$tas$lat,
+                             Variable = list(varName = "malar_suit", level = NULL),
+                             Dates = out$tas$Dates,
+                             Datasets = out$tas$Datasets,
+                             when = Sys.time(),
+                             source_files = "see source files of tas, tdps and pr")
 
 attr(out$malariaSuit$Variable, "units") <- "none"
 attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission"
 
 # Compute number of number of months per year that are suitable
 # (i.e. suitability = 1)
-malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, 
-                                                            threshold=0.5)
+malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, threshold = 0.5)
 
 # Sneak peek of the data
-malariaInd$data[1,1,1,,][15:20, 15:20]
+malariaInd$data[1, 1, 1, , ][15:20, 15:20]
 
 summary(malariaInd$data)
 ```
 
 ## 4. Results
 ### 4.1. Map
-Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to 
-have a visual inspection of the number of suitable months of a particular year.
-In this, the first and last years of the time series.
+Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to have a visual inspection of the number of suitable months of a particular year. In this, the first and last years of the time series.
 
 ```{r echo=FALSE}
 # Source master branch for PlotLayout()
@@ -273,61 +225,55 @@ clim.palette <- s2dv::clim.palette
 .IsColor<- s2dv:::.IsColor
 ```
 
-```{r fig.width=7, fig.height=4}
+```{r fig.width = 7, fig.height = 4}
 PlotLayout(PlotEquiMap, c('lat', 'lon'),
-           var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
-           nrow=1, ncol=2,
-           lon=malariaInd$lon,
-           lat=malariaInd$lat,
-           filled.continents=FALSE,
-           brks=seq(0, 12, by=1),
-           toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula",
-           titles=c(as.character(year_in), as.character(year_fi)),
-           title_scale=0.4,
-           coast_width=2,
-           filled.oceans=TRUE,
-           country.borders=TRUE,
-           intylat=1,
-           intxlon=1)
+           var = malariaInd$data[1, 1, c(1,dim(malariaInd$data)[[3]]), , ],
+           nrow = 1, ncol = 2,
+           lon = malariaInd$lon,
+           lat = malariaInd$lat,
+           filled.continents = FALSE,
+           brks = seq(0, 12, by = 1),
+           toptitle = "Number of months per year suitable for malaria transmission in the Iberian Peninsula",
+           titles = c(as.character(year_in), as.character(year_fi)),
+           title_scale = 0.4,
+           coast_width = 2,
+           filled.oceans = TRUE,
+           country.borders = TRUE,
+           intylat = 1,
+           intxlon = 1)
 ```
 
 ### 4.2 Time series
-The annual mean number of months suitable for malaria transmission can be extracted
-with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created
-with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()`
-function allows the user to compute a linear trend in the data.
+The annual mean number of months suitable for malaria transmission can be extracted with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()` function allows the user to compute a linear trend in the data.
 
 ```{r fig.width=7, fig.height=4}
 library(dplyr)
 library(ggplot2)
 
-data.frame(year=seq(year_in, year_fi, by=1),
-           ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
-  ggplot(aes(x=year, y=ind)) +
-  geom_line() +
-  scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) +
-  geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) +
-  labs(x="Year", y="Mean number of months suitable \n for malaria transmission") +
-  theme_bw()
+data.frame(year = seq(year_in, year_fi, by = 1),
+           ind = c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm = TRUE))) %>% 
+           ggplot(aes(x = year, y = ind)) + 
+           geom_line() + 
+           scale_x_continuous(breaks = seq(year_in, year_fi, by = 1)) + 
+           geom_smooth(method = 'lm', formula = y ~ x, alpha = 0.3, linetype = "dashed", size = 0.3, se = FALSE) + 
+           labs(x = "Year", y = "Mean number of months suitable \n for malaria transmission") + 
+           theme_bw()
 ```
 
 ### 4.3 Percentage change
 ```{r}
-first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE)
-last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE)
-change <- round((last-first)/first * 100, 0)
+first <- mean(malariaInd$data[1, 1, 1, , ], na.rm = TRUE)
+last <- mean(malariaInd$data[1, 1, dim(malariaInd$data)[[3]], , ], na.rm = TRUE)
+change <- round((last - first) / first * 100, 0)
 change
 ```
 
 ## 6. Conclusions
-The number of months suitable for malaria transmission appear to have increased 
-in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with
-the year 2010, there was a `r change`% increase in the number of months suitable for
-malaria transmission in the region.
+The number of months suitable for malaria transmission appear to have increased in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with the year 2010, there was a `r change`% increase in the number of months suitable for malaria transmission.
 
 ## References
 
-* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL]("https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml")
+* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL](https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml)
 * Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. _Remote Sens Environ. 2014;145:116-130._ DOI:10.1016/J.RSE.2014.01.014
 * Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. _Front Med. 2021; 8:876._ DOI:10.3389/FMED.2021.691095/BIBTEX
 * Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. _Travel Med Infect Dis. 2020;36:101815._ DOI:10.1016/J.TMAID.2020.101815
-- 
GitLab


From 4e43130bcab1f42c73ab6dca1f06c5ecd46bbbb6 Mon Sep 17 00:00:00 2001
From: Eva Rifa 
Date: Wed, 31 Aug 2022 13:20:16 +0200
Subject: [PATCH 12/29] Correct formatting

---
 vignettes/MalariaIndicator.Rmd | 222 +++++++++++++--------------------
 1 file changed, 84 insertions(+), 138 deletions(-)

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index 534a71d..34f6729 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -9,12 +9,6 @@ vignette: >
   %\usepackage[utf8]{inputenc}
 ---
 
-
-
 ```{r echo=FALSE}
 knitr::opts_chunk$set(warning=FALSE, message=FALSE)
 ```
@@ -25,42 +19,18 @@ Climate Suitability for Malaria Transmission
 
 ## Introduction
 
-Climate sensitive infectious diseases are a subject of major concern in the context 
-of a changing climate. Malaria is an infectious 
-disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ 
-mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete 
-elimination of endemic  circulation of malaria in Europe, although sporadic transmission 
-events are frequently reported in travelers coming from endemic areas. Studies 
-evaluating drivers of malaria elimination found that high socio-economic 
-conditions and increased life expectancy are key determinants in keeping this disease
-out of the continent  (Fischer et al., 2020). On the 
-other hand, the risk of malaria resurgence is present as climatic conditions 
-become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
-
-The [Lancet Countdown in Europe collaboration]("https://www.lancetcountdown.org/europe/") 
-was launched in 2021 with the purpose of using a variety of indicators to monitor 
-trends in the impact of climate on dynamic of different health processes. As 
-part of this initiative, the Climate Suitability for Malaria Transmission indicator 
-was adapted from the [Global Lancet Countdown Collaboration]("https://www.lancetcountdown.org/") 
-(Romanello et al., 2021) 
-to track the annual number of months suitable of malaria transmission between 1950 
-and 2021.This indicator is a threshold based model that 
-overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
-_Plasmodium vivax_, which was the endemic parasite in the continent 
-until the mid 1970s (Boualam et al., 2021).
-
-Although the `CSIndicators` was initially developed for providing a set of tools
-useful in the context of agricultural practices, it is also possible to expand
-its applications to the computation of other type of indicators. In this vignette,
-the Climate Suitability  for Malaria indicator will be computed using  the `TotalTimeExceedingThreshold()` 
-function. The output will be an multidimensional array containing the number of 
+Climate sensitive infectious diseases are a subject of major concern in the context of a changing climate. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic circulation of malaria in Europe, although sporadic transmission events are frequently reported in travelers coming from endemic areas. Studies evaluating drivers of malaria elimination found that high socio-economic conditions and increased life expectancy are key determinants in keeping this disease out of the continent (Fischer et al., 2020). On the other hand, the risk of malaria resurgence is present as climatic conditions become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
+
+The [Lancet Countdown in Europe collaboration](https://www.lancetcountdown.org/europe/) was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on dynamic of different health processes. As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the [Global Lancet Countdown Collaboration](https://www.lancetcountdown.org/) (Romanello et al., 2021) to track the annual number of months suitable of malaria transmission between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
+_Plasmodium vivax_, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021).
+
+Although the `CSIndicators` was initially developed for providing a set of tools useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold()` function. The output will be an multidimensional array containing the number of 
 months per year suitable for malaria transmission.
 
 
 ## Load libraries
 
-In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will
-be used for data extraction and visualization.
+In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will be used for data extraction and visualization.
 
 ```{r eval=FALSE}
 library(CSTools)
@@ -70,7 +40,7 @@ library(s2dv)
 
 
 ```{r echo=FALSE}
-#Temporary links to functions
+# Temporary links to functions
 library(CSIndicators)
 source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R")
 ```
@@ -78,8 +48,7 @@ source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from
 
 ## 1. Data
 ### 1.1 Area of interest
-The number of months suitable for malaria transmission between 2010 and 2020 will
-be computed for the Iberian Peninsula.
+The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.
 
 ```{r echo=FALSE}
 knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
@@ -87,27 +56,15 @@ knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/m
 
 ### 1.2 Data extraction
 The Malaria indicator is computed with climate variables extracted from the 
-[ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950
-and present day, at a 9km resolution. These variables are 2 meter 
-temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation
- (`prlr`). These data are produced 
-by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** 
-from the **Barcelona Supercomputing Center**.
-
-The function `CST_Load` from the `CSTools` package allows the user to access the
-processed data and delivers the data in the form of an `s2dv_cube` object. These
-multidimensional objects store data and metadata in several elements. For further 
-information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
-
-`CST_Load` has its requirements for extracting data. First, the path to the files 
-is introduced using *whitecards* delimited by dollar signs. Second, the temporal 
-extent and resolution of the output con be provided with `sdates`, which takes a 
-vector of all dates of interest. The spatial extent is inputted within `lonmax`, 
-`lonmin`, `latmax` and `latmin.`
+[ERA5-Land reanalysis dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. These variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation (`prlr`). These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**.
+
+The function `CST_Load` from the `CSTools` package allows the user to access the processed data and delivers the data in the form of an `s2dv_cube` object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
+
+`CST_Load` has its requirements for extracting data. First, the path to the files is introduced using *whitecards* delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with `sdates`, which takes a vector of all dates of interest. The spatial extent is inputted within `lonmax`, `lonmin`, `latmax` and `latmin.`
 
 ```{r}
 # Path to file locations using whitecards
-path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
+path_ERA5_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
 
 # Temporal extent and resolution:
 # We will work with all months from 2010 to 2020
@@ -124,15 +81,15 @@ vars <- c('tas', 'tdps', 'prlr')
 out <- NULL
 for(var in vars){
   out[[var]] <- CST_Load_s2dv(var = var,
-                           exp = NULL, # We only require observed data
-                           obs = list(path_ERA5_CDS),
-                           sdates = sdates,
-                           lonmax = 5, lonmin = 350,
-                           latmax = 45, latmin = 35,
-                           storefreq = 'monthly',
-                           leadtimemin = month_in, 
-                           leadtimemax = month_fi,
-                           output = "lonlat")  
+                              exp = NULL, # We only require observed data
+                              obs = list(path_ERA5_CDS),
+                              sdates = sdates,
+                              lonmax = 5, lonmin = 350,
+                              latmax = 45, latmin = 35,
+                              storefreq = 'monthly',
+                              leadtimemin = month_in, 
+                              leadtimemax = month_fi,
+                              output = "lonlat")  
 }
 
 # library(zeallot)
@@ -160,7 +117,7 @@ summary(out$tas$data)
 
 ```{r}
 # Sneak peek of a sample of the temperature data in Jan 2010
-out$tas$data[1,1,1,1,,][15:20, 15:20]
+out$tas$data[1, 1, 1, 1, , ][15:20, 15:20]
 ```
 
 ## 2. Data transformation
@@ -175,25 +132,25 @@ summary(out$tas$data)
 ```
 
 ### 2.3 Relative humidity
-Then, it is possible to calculate relative humidity (hurs) using temperature ($T$) 
-and dew point temperature ($T_d$) with the August-Roche-Magnus equation (Alduchov 1996):
+Then, it is possible to calculate relative humidity (hurs) using temperature ($`T`$) and dew point temperature ($`T_d`$) with the August-Roche-Magnus equation (Alduchov 1996):
 
-$$
+```math
 RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}
-$$
-Where _a_ and _b_ are the coefficients 17.625 and 243.04, respectively.
+```
+
+Where ($`a`$) and ($`b`$) are the coefficients 17.625 and 243.04, respectively.
 
 ```{r}
 # Create a new s2dv object with relative humidity
-out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
-                                  exp((17.625*out$tas$data) / (243.04+out$tas$data))),
-                      lon=out$tas$lon,
-                      lat=out$tas$lat,
-                      Variable=list(varName="rh", level=NULL),
-                      Dates=out$tas$Dates,
-                      Datasets=out$tas$Datasets,
-                      when=Sys.time(),
-                      source_files="see source files of tas, tdps and pr")
+out$hurs <- s2dv_cube(data = 100*(exp((17.625 * out$tdps$data) / (243.04 + out$tdps$data)) / 
+                                  exp((17.625 * out$tas$data) / (243.04 + out$tas$data))),
+                      lon = out$tas$lon,
+                      lat = out$tas$lat,
+                      Variable = list(varName = "rh", level =NULL),
+                      Dates = out$tas$Dates,
+                      Datasets = out$tas$Datasets,
+                      when = Sys.time(),
+                      source_files = "see source files of tas, tdps and pr")
 
 attr(out$hurs$Variable, "units") <- "%"
 attr(out$hurs$Variable, "longname") <- "near-surface relative humidity"
@@ -202,16 +159,14 @@ summary(out$hurs$data)
 ```
 
 ### 2.2 Precipitation
-Precipitation is extracted from the hourly reanalaysis dataset and delivered in
-monthly meters per second. The next step is to transform it into accumulated mm 
-per month
+Precipitation is extracted from the hourly reanalaysis dataset and delivered in monthly meters per second. The next step is to transform it into accumulated mm per month
 ```{r}
 # To compute the accumulated precipitation per month it is necessary to multiply by:
 # - total number of seconds in one hour: 3600
 # - total number of hours in a day: 24
 # - average number of days in a month: 365.25/12=30.44
 # - change factor from meter to mm: 1000
-out$prlr$data <- out$prlr$data*3600*24*30.44*1000
+out$prlr$data <- out$prlr$data * 3600 * 24 * 30.44 * 1000
 
 summary(out$prlr$data)
 ```
@@ -226,37 +181,34 @@ suitable for transmission of _P. vivax_ according to the following thresholds:
 
 ```{r}
 # Create a new s2dv object with malaria suitability
-out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & 
-                                  out$tas$data <= 33 & 
-                                  out$hurs$data >= 60 & 
-                                  out$prlr$data >= 80, 1, 0),
-                    lon=out$tas$lon,
-                    lat=out$tas$lat,
-                    Variable=list(varName="malar_suit", level=NULL),
-                    Dates=out$tas$Dates,
-                    Datasets=out$tas$Datasets,
-                    when=Sys.time(),
-                    source_files="see source files of tas, tdps and pr")
+out$malariaSuit <- s2dv_cube(data = ifelse(out$tas$data >= 14.5 & 
+                                    out$tas$data <= 33 & 
+                                    out$hurs$data >= 60 & 
+                                    out$prlr$data >= 80, 1, 0),
+                             lon = out$tas$lon,
+                             lat = out$tas$lat,
+                             Variable = list(varName = "malar_suit", level = NULL),
+                             Dates = out$tas$Dates,
+                             Datasets = out$tas$Datasets,
+                             when = Sys.time(),
+                             source_files = "see source files of tas, tdps and pr")
 
 attr(out$malariaSuit$Variable, "units") <- "none"
 attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission"
 
 # Compute number of number of months per year that are suitable
 # (i.e. suitability = 1)
-malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, 
-                                                            threshold=0.5)
+malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, threshold = 0.5)
 
 # Sneak peek of the data
-malariaInd$data[1,1,1,,][15:20, 15:20]
+malariaInd$data[1, 1, 1, , ][15:20, 15:20]
 
 summary(malariaInd$data)
 ```
 
 ## 4. Results
 ### 4.1. Map
-Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to 
-have a visual inspection of the number of suitable months of a particular year.
-In this, the first and last years of the time series.
+Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to have a visual inspection of the number of suitable months of a particular year. In this, the first and last years of the time series.
 
 ```{r echo=FALSE}
 # Source master branch for PlotLayout()
@@ -273,61 +225,55 @@ clim.palette <- s2dv::clim.palette
 .IsColor<- s2dv:::.IsColor
 ```
 
-```{r fig.width=7, fig.height=4}
+```{r fig.width = 7, fig.height = 4}
 PlotLayout(PlotEquiMap, c('lat', 'lon'),
-           var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
-           nrow=1, ncol=2,
-           lon=malariaInd$lon,
-           lat=malariaInd$lat,
-           filled.continents=FALSE,
-           brks=seq(0, 12, by=1),
-           toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula",
-           titles=c(as.character(year_in), as.character(year_fi)),
-           title_scale=0.4,
-           coast_width=2,
-           filled.oceans=TRUE,
-           country.borders=TRUE,
-           intylat=1,
-           intxlon=1)
+           var = malariaInd$data[1, 1, c(1,dim(malariaInd$data)[[3]]), , ],
+           nrow = 1, ncol = 2,
+           lon = malariaInd$lon,
+           lat = malariaInd$lat,
+           filled.continents = FALSE,
+           brks = seq(0, 12, by = 1),
+           toptitle = "Number of months per year suitable for malaria transmission in the Iberian Peninsula",
+           titles = c(as.character(year_in), as.character(year_fi)),
+           title_scale = 0.4,
+           coast_width = 2,
+           filled.oceans = TRUE,
+           country.borders = TRUE,
+           intylat = 1,
+           intxlon = 1)
 ```
 
 ### 4.2 Time series
-The annual mean number of months suitable for malaria transmission can be extracted
-with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created
-with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()`
-function allows the user to compute a linear trend in the data.
+The annual mean number of months suitable for malaria transmission can be extracted with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()` function allows the user to compute a linear trend in the data.
 
 ```{r fig.width=7, fig.height=4}
 library(dplyr)
 library(ggplot2)
 
-data.frame(year=seq(year_in, year_fi, by=1),
-           ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
-  ggplot(aes(x=year, y=ind)) +
-  geom_line() +
-  scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) +
-  geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) +
-  labs(x="Year", y="Mean number of months suitable \n for malaria transmission") +
-  theme_bw()
+data.frame(year = seq(year_in, year_fi, by = 1),
+           ind = c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm = TRUE))) %>% 
+           ggplot(aes(x = year, y = ind)) + 
+           geom_line() + 
+           scale_x_continuous(breaks = seq(year_in, year_fi, by = 1)) + 
+           geom_smooth(method = 'lm', formula = y ~ x, alpha = 0.3, linetype = "dashed", size = 0.3, se = FALSE) + 
+           labs(x = "Year", y = "Mean number of months suitable \n for malaria transmission") + 
+           theme_bw()
 ```
 
 ### 4.3 Percentage change
 ```{r}
-first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE)
-last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE)
-change <- round((last-first)/first * 100, 0)
+first <- mean(malariaInd$data[1, 1, 1, , ], na.rm = TRUE)
+last <- mean(malariaInd$data[1, 1, dim(malariaInd$data)[[3]], , ], na.rm = TRUE)
+change <- round((last - first) / first * 100, 0)
 change
 ```
 
 ## 6. Conclusions
-The number of months suitable for malaria transmission appear to have increased 
-in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with
-the year 2010, there was a `r change`% increase in the number of months suitable for
-malaria transmission.
+The number of months suitable for malaria transmission appear to have increased in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with the year 2010, there was a `r change`% increase in the number of months suitable for malaria transmission.
 
 ## References
 
-* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL]("https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml")
+* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL](https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml)
 * Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. _Remote Sens Environ. 2014;145:116-130._ DOI:10.1016/J.RSE.2014.01.014
 * Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. _Front Med. 2021; 8:876._ DOI:10.3389/FMED.2021.691095/BIBTEX
 * Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. _Travel Med Infect Dis. 2020;36:101815._ DOI:10.1016/J.TMAID.2020.101815
-- 
GitLab


From e84f985abe7afacb17c9c5e526c6c9ff578a3915 Mon Sep 17 00:00:00 2001
From: Eva Rifa 
Date: Wed, 31 Aug 2022 13:26:50 +0200
Subject: [PATCH 13/29] Try to fix commit by adding a comment

---
 vignettes/MalariaIndicator.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index 34f6729..af7272b 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -51,7 +51,7 @@ source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from
 The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.
 
 ```{r echo=FALSE}
-knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
+# knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
 ```
 
 ### 1.2 Data extraction
-- 
GitLab


From d7ef867600f2959e7006cb89ce54db8c6dfe5af9 Mon Sep 17 00:00:00 2001
From: Eva Rifa 
Date: Wed, 31 Aug 2022 14:58:21 +0200
Subject: [PATCH 14/29] Fix pipeline

---
 vignettes/MalariaIndicator.Rmd | 224 ++++++++++++++++++++-------------
 1 file changed, 139 insertions(+), 85 deletions(-)

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index af7272b..2194069 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -9,6 +9,12 @@ vignette: >
   %\usepackage[utf8]{inputenc}
 ---
 
+
+
 ```{r echo=FALSE}
 knitr::opts_chunk$set(warning=FALSE, message=FALSE)
 ```
@@ -19,18 +25,42 @@ Climate Suitability for Malaria Transmission
 
 ## Introduction
 
-Climate sensitive infectious diseases are a subject of major concern in the context of a changing climate. Malaria is an infectious disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete elimination of endemic circulation of malaria in Europe, although sporadic transmission events are frequently reported in travelers coming from endemic areas. Studies evaluating drivers of malaria elimination found that high socio-economic conditions and increased life expectancy are key determinants in keeping this disease out of the continent (Fischer et al., 2020). On the other hand, the risk of malaria resurgence is present as climatic conditions become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
-
-The [Lancet Countdown in Europe collaboration](https://www.lancetcountdown.org/europe/) was launched in 2021 with the purpose of using a variety of indicators to monitor trends in the impact of climate on dynamic of different health processes. As part of this initiative, the Climate Suitability for Malaria Transmission indicator was adapted from the [Global Lancet Countdown Collaboration](https://www.lancetcountdown.org/) (Romanello et al., 2021) to track the annual number of months suitable of malaria transmission between 1950 and 2021.This indicator is a threshold based model that overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
-_Plasmodium vivax_, which was the endemic parasite in the continent until the mid 1970s (Boualam et al., 2021).
-
-Although the `CSIndicators` was initially developed for providing a set of tools useful in the context of agricultural practices, it is also possible to expand its applications to the computation of other type of indicators. In this vignette, the Climate Suitability for Malaria indicator will be computed using the `TotalTimeExceedingThreshold()` function. The output will be an multidimensional array containing the number of 
+Climate sensitive infectious diseases are a subject of major concern in the context 
+of a changing climate. Malaria is an infectious 
+disease caused by parasites of the genus _Plasmodium_ and transmitted by _Anopheles_ 
+mosquitoes (Boualam et al., 2021). Eradication efforts managed to achieve complete 
+elimination of endemic  circulation of malaria in Europe, although sporadic transmission 
+events are frequently reported in travelers coming from endemic areas. Studies 
+evaluating drivers of malaria elimination found that high socio-economic 
+conditions and increased life expectancy are key determinants in keeping this disease
+out of the continent  (Fischer et al., 2020). On the 
+other hand, the risk of malaria resurgence is present as climatic conditions 
+become more suitable for both the parasite and mosquitoes transmission (Zhao et al., 2016). 
+
+The [Lancet Countdown in Europe collaboration]("https://www.lancetcountdown.org/europe/") 
+was launched in 2021 with the purpose of using a variety of indicators to monitor 
+trends in the impact of climate on dynamic of different health processes. As 
+part of this initiative, the Climate Suitability for Malaria Transmission indicator 
+was adapted from the [Global Lancet Countdown Collaboration]("https://www.lancetcountdown.org/") 
+(Romanello et al., 2021) 
+to track the annual number of months suitable of malaria transmission between 1950 
+and 2021.This indicator is a threshold based model that 
+overlaps the climatic and environmental requirements of _Anopheles_ mosquitoes and 
+_Plasmodium vivax_, which was the endemic parasite in the continent 
+until the mid 1970s (Boualam et al., 2021).
+
+Although the `CSIndicators` was initially developed for providing a set of tools
+useful in the context of agricultural practices, it is also possible to expand
+its applications to the computation of other type of indicators. In this vignette,
+the Climate Suitability  for Malaria indicator will be computed using  the `TotalTimeExceedingThreshold()` 
+function. The output will be an multidimensional array containing the number of 
 months per year suitable for malaria transmission.
 
 
 ## Load libraries
 
-In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will be used for data extraction and visualization.
+In addition to the `CSIndicators` package, the `CSTools` and `s2dv` packages will
+be used for data extraction and visualization.
 
 ```{r eval=FALSE}
 library(CSTools)
@@ -40,7 +70,7 @@ library(s2dv)
 
 
 ```{r echo=FALSE}
-# Temporary links to functions
+#Temporary links to functions
 library(CSIndicators)
 source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from_s2dv.R")
 ```
@@ -48,23 +78,36 @@ source("https://earth.bsc.es/gitlab/nperez/Flor/-/raw/master/CST_Load_devel_from
 
 ## 1. Data
 ### 1.1 Area of interest
-The number of months suitable for malaria transmission between 2010 and 2020 will be computed for the Iberian Peninsula.
+The number of months suitable for malaria transmission between 2010 and 2020 will
+be computed for the Iberian Peninsula.
 
 ```{r echo=FALSE}
-# knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
+knitr::include_url("https://es.wikipedia.org/wiki/Pen%C3%ADnsula_ib%C3%A9rica#/media/Archivo:Espa%C3%B1a_y_Portugal.jpg")
 ```
 
 ### 1.2 Data extraction
 The Malaria indicator is computed with climate variables extracted from the 
-[ERA5-Land reanalysis dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview) (Muñoz-Sabater et al., 2021), which span between 1950 and present day, at a 9km resolution. These variables are 2 meter temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation (`prlr`). These data are produced by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** from the **Barcelona Supercomputing Center**.
-
-The function `CST_Load` from the `CSTools` package allows the user to access the processed data and delivers the data in the form of an `s2dv_cube` object. These multidimensional objects store data and metadata in several elements. For further information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
-
-`CST_Load` has its requirements for extracting data. First, the path to the files is introduced using *whitecards* delimited by dollar signs. Second, the temporal extent and resolution of the output con be provided with `sdates`, which takes a vector of all dates of interest. The spatial extent is inputted within `lonmax`, `lonmin`, `latmax` and `latmin.`
+[ERA5-Land reanalysis dataset]("https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview") (Muñoz-Sabater et al., 2021), which span between 1950
+and present day, at a 9km resolution. These variables are 2 meter 
+temperature (`tas`), 2 meter dew point temperature (`tdps`) and total precipitation
+ (`prlr`). These data are produced 
+by the Copernicus service and extracted and reprocessed by the **Earth Sciences department** 
+from the **Barcelona Supercomputing Center**.
+
+The function `CST_Load` from the `CSTools` package allows the user to access the
+processed data and delivers the data in the form of an `s2dv_cube` object. These
+multidimensional objects store data and metadata in several elements. For further 
+information about these objects visit [Data retrieval and storage](https://cran.r-project.org/package=CSTools/vignettes/Data_Considerations.html). 
+
+`CST_Load` has its requirements for extracting data. First, the path to the files 
+is introduced using *whitecards* delimited by dollar signs. Second, the temporal 
+extent and resolution of the output con be provided with `sdates`, which takes a 
+vector of all dates of interest. The spatial extent is inputted within `lonmax`, 
+`lonmin`, `latmax` and `latmin.`
 
 ```{r}
 # Path to file locations using whitecards
-path_ERA5_CDS <- list(path = '/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
+path_ERA5_CDS <- list(path='/esarchive/recon/ecmwf/era5land/$STORE_FREQ$_mean/$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')
 
 # Temporal extent and resolution:
 # We will work with all months from 2010 to 2020
@@ -81,15 +124,15 @@ vars <- c('tas', 'tdps', 'prlr')
 out <- NULL
 for(var in vars){
   out[[var]] <- CST_Load_s2dv(var = var,
-                              exp = NULL, # We only require observed data
-                              obs = list(path_ERA5_CDS),
-                              sdates = sdates,
-                              lonmax = 5, lonmin = 350,
-                              latmax = 45, latmin = 35,
-                              storefreq = 'monthly',
-                              leadtimemin = month_in, 
-                              leadtimemax = month_fi,
-                              output = "lonlat")  
+                           exp = NULL, # We only require observed data
+                           obs = list(path_ERA5_CDS),
+                           sdates = sdates,
+                           lonmax = 5, lonmin = 350,
+                           latmax = 45, latmin = 35,
+                           storefreq = 'monthly',
+                           leadtimemin = month_in, 
+                           leadtimemax = month_fi,
+                           output = "lonlat")  
 }
 
 # library(zeallot)
@@ -117,7 +160,7 @@ summary(out$tas$data)
 
 ```{r}
 # Sneak peek of a sample of the temperature data in Jan 2010
-out$tas$data[1, 1, 1, 1, , ][15:20, 15:20]
+out$tas$data[1,1,1,1,,][15:20, 15:20]
 ```
 
 ## 2. Data transformation
@@ -132,25 +175,25 @@ summary(out$tas$data)
 ```
 
 ### 2.3 Relative humidity
-Then, it is possible to calculate relative humidity (hurs) using temperature ($`T`$) and dew point temperature ($`T_d`$) with the August-Roche-Magnus equation (Alduchov 1996):
+Then, it is possible to calculate relative humidity (hurs) using temperature ($T$) 
+and dew point temperature ($T_d$) with the August-Roche-Magnus equation (Alduchov 1996):
 
-```math
+$$
 RH=100*\frac{exp(\frac{aT_d}{b+T_d})}{exp(\frac{aT}{c+T})}
-```
-
-Where ($`a`$) and ($`b`$) are the coefficients 17.625 and 243.04, respectively.
+$$
+Where _a_ and _b_ are the coefficients 17.625 and 243.04, respectively.
 
 ```{r}
 # Create a new s2dv object with relative humidity
-out$hurs <- s2dv_cube(data = 100*(exp((17.625 * out$tdps$data) / (243.04 + out$tdps$data)) / 
-                                  exp((17.625 * out$tas$data) / (243.04 + out$tas$data))),
-                      lon = out$tas$lon,
-                      lat = out$tas$lat,
-                      Variable = list(varName = "rh", level =NULL),
-                      Dates = out$tas$Dates,
-                      Datasets = out$tas$Datasets,
-                      when = Sys.time(),
-                      source_files = "see source files of tas, tdps and pr")
+out$hurs <- s2dv_cube(data=100*(exp((17.625*out$tdps$data)/(243.04+out$tdps$data)) / 
+                                  exp((17.625*out$tas$data) / (243.04+out$tas$data))),
+                      lon=out$tas$lon,
+                      lat=out$tas$lat,
+                      Variable=list(varName="rh", level=NULL),
+                      Dates=out$tas$Dates,
+                      Datasets=out$tas$Datasets,
+                      when=Sys.time(),
+                      source_files="see source files of tas, tdps and pr")
 
 attr(out$hurs$Variable, "units") <- "%"
 attr(out$hurs$Variable, "longname") <- "near-surface relative humidity"
@@ -159,14 +202,16 @@ summary(out$hurs$data)
 ```
 
 ### 2.2 Precipitation
-Precipitation is extracted from the hourly reanalaysis dataset and delivered in monthly meters per second. The next step is to transform it into accumulated mm per month
+Precipitation is extracted from the hourly reanalaysis dataset and delivered in
+monthly meters per second. The next step is to transform it into accumulated mm 
+per month
 ```{r}
 # To compute the accumulated precipitation per month it is necessary to multiply by:
 # - total number of seconds in one hour: 3600
 # - total number of hours in a day: 24
 # - average number of days in a month: 365.25/12=30.44
 # - change factor from meter to mm: 1000
-out$prlr$data <- out$prlr$data * 3600 * 24 * 30.44 * 1000
+out$prlr$data <- out$prlr$data*3600*24*30.44*1000
 
 summary(out$prlr$data)
 ```
@@ -181,34 +226,37 @@ suitable for transmission of _P. vivax_ according to the following thresholds:
 
 ```{r}
 # Create a new s2dv object with malaria suitability
-out$malariaSuit <- s2dv_cube(data = ifelse(out$tas$data >= 14.5 & 
-                                    out$tas$data <= 33 & 
-                                    out$hurs$data >= 60 & 
-                                    out$prlr$data >= 80, 1, 0),
-                             lon = out$tas$lon,
-                             lat = out$tas$lat,
-                             Variable = list(varName = "malar_suit", level = NULL),
-                             Dates = out$tas$Dates,
-                             Datasets = out$tas$Datasets,
-                             when = Sys.time(),
-                             source_files = "see source files of tas, tdps and pr")
+out$malariaSuit <- s2dv_cube(data=ifelse(out$tas$data >= 14.5 & 
+                                  out$tas$data <= 33 & 
+                                  out$hurs$data >= 60 & 
+                                  out$prlr$data >= 80, 1, 0),
+                    lon=out$tas$lon,
+                    lat=out$tas$lat,
+                    Variable=list(varName="malar_suit", level=NULL),
+                    Dates=out$tas$Dates,
+                    Datasets=out$tas$Datasets,
+                    when=Sys.time(),
+                    source_files="see source files of tas, tdps and pr")
 
 attr(out$malariaSuit$Variable, "units") <- "none"
 attr(out$malariaSuit$Variable, "longname") <- "suitability for malaria transmission"
 
 # Compute number of number of months per year that are suitable
 # (i.e. suitability = 1)
-malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, threshold = 0.5)
+malariaInd <- CSIndicators::CST_TotalTimeExceedingThreshold(out$malariaSuit, 
+                                                            threshold=0.5)
 
 # Sneak peek of the data
-malariaInd$data[1, 1, 1, , ][15:20, 15:20]
+malariaInd$data[1,1,1,,][15:20, 15:20]
 
 summary(malariaInd$data)
 ```
 
 ## 4. Results
 ### 4.1. Map
-Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to have a visual inspection of the number of suitable months of a particular year. In this, the first and last years of the time series.
+Using the function `PlotEquiMap()` from the `s2dv` package, it is possible to 
+have a visual inspection of the number of suitable months of a particular year.
+In this, the first and last years of the time series.
 
 ```{r echo=FALSE}
 # Source master branch for PlotLayout()
@@ -225,55 +273,61 @@ clim.palette <- s2dv::clim.palette
 .IsColor<- s2dv:::.IsColor
 ```
 
-```{r fig.width = 7, fig.height = 4}
+```{r fig.width=7, fig.height=4}
 PlotLayout(PlotEquiMap, c('lat', 'lon'),
-           var = malariaInd$data[1, 1, c(1,dim(malariaInd$data)[[3]]), , ],
-           nrow = 1, ncol = 2,
-           lon = malariaInd$lon,
-           lat = malariaInd$lat,
-           filled.continents = FALSE,
-           brks = seq(0, 12, by = 1),
-           toptitle = "Number of months per year suitable for malaria transmission in the Iberian Peninsula",
-           titles = c(as.character(year_in), as.character(year_fi)),
-           title_scale = 0.4,
-           coast_width = 2,
-           filled.oceans = TRUE,
-           country.borders = TRUE,
-           intylat = 1,
-           intxlon = 1)
+           var=malariaInd$data[1,1,c(1,dim(malariaInd$data)[[3]]),,],
+           nrow=1, ncol=2,
+           lon=malariaInd$lon,
+           lat=malariaInd$lat,
+           filled.continents=FALSE,
+           brks=seq(0, 12, by=1),
+           toptitle="Number of months per year suitable for malaria transmission in the Iberian Peninsula",
+           titles=c(as.character(year_in), as.character(year_fi)),
+           title_scale=0.4,
+           coast_width=2,
+           filled.oceans=TRUE,
+           country.borders=TRUE,
+           intylat=1,
+           intxlon=1)
 ```
 
 ### 4.2 Time series
-The annual mean number of months suitable for malaria transmission can be extracted with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()` function allows the user to compute a linear trend in the data.
+The annual mean number of months suitable for malaria transmission can be extracted
+with the `MeanDims()` function from the `s2dv` package. Then, a plot can be created
+with functions from the `dplyr` and `ggplot2` packages. Additionally, the `ggplot()`
+function allows the user to compute a linear trend in the data.
 
 ```{r fig.width=7, fig.height=4}
 library(dplyr)
 library(ggplot2)
 
-data.frame(year = seq(year_in, year_fi, by = 1),
-           ind = c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm = TRUE))) %>% 
-           ggplot(aes(x = year, y = ind)) + 
-           geom_line() + 
-           scale_x_continuous(breaks = seq(year_in, year_fi, by = 1)) + 
-           geom_smooth(method = 'lm', formula = y ~ x, alpha = 0.3, linetype = "dashed", size = 0.3, se = FALSE) + 
-           labs(x = "Year", y = "Mean number of months suitable \n for malaria transmission") + 
-           theme_bw()
+data.frame(year=seq(year_in, year_fi, by=1),
+           ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
+  ggplot(aes(x=year, y=ind)) +
+  geom_line() +
+  scale_x_continuous(breaks=seq(year_in, year_fi, by=1)) +
+  geom_smooth(method='lm', formula= y~x, alpha=0.3, linetype="dashed", size=0.3, se=FALSE) +
+  labs(x="Year", y="Mean number of months suitable \n for malaria transmission") +
+  theme_bw()
 ```
 
 ### 4.3 Percentage change
 ```{r}
-first <- mean(malariaInd$data[1, 1, 1, , ], na.rm = TRUE)
-last <- mean(malariaInd$data[1, 1, dim(malariaInd$data)[[3]], , ], na.rm = TRUE)
-change <- round((last - first) / first * 100, 0)
+first <- mean(malariaInd$data[1,1,1,,], na.rm=TRUE)
+last <- mean(malariaInd$data[1,1,dim(malariaInd$data)[[3]],,], na.rm=TRUE)
+change <- round((last-first)/first * 100, 0)
 change
 ```
 
 ## 6. Conclusions
-The number of months suitable for malaria transmission appear to have increased in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with the year 2010, there was a `r change`% increase in the number of months suitable for malaria transmission.
+The number of months suitable for malaria transmission appear to have increased 
+in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with
+the year 2010, there was a `r change`% increase in the number of months suitable for
+malaria transmission in the region.
 
 ## References
 
-* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL](https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml)
+* Alduchov O. Improved Magnus Form Approximation of Saturation Vapor Pressure. _J Appl Meteorl Climatol. 1996; 35(4),601-609._ [URL]("https://journals.ametsoc.org/view/journals/apme/35/4/1520-0450_1996_035_0601_imfaos_2_0_co_2.xml")
 * Benali A, Nunes JP, Freitas FB, et al. Satellite-derived estimation of environmental suitability for malaria vector development in Portugal. _Remote Sens Environ. 2014;145:116-130._ DOI:10.1016/J.RSE.2014.01.014
 * Boualam MA, Pradines B, Drancourt M, Barbieri R. Malaria in Europe: a historical perspective. _Front Med. 2021; 8:876._ DOI:10.3389/FMED.2021.691095/BIBTEX
 * Fischer L, Gültekin N, Kaelin M, Fehr J, Schlagenhauf P. Rising temperature and its impact on receptivity to malaria transmission in Europe: a systematic review. _Travel Med Infect Dis. 2020;36:101815._ DOI:10.1016/J.TMAID.2020.101815
-- 
GitLab


From 6a9afea1381c5a5c10c740b0e805eb4752884775 Mon Sep 17 00:00:00 2001
From: mlotto 
Date: Wed, 31 Aug 2022 15:28:50 +0200
Subject: [PATCH 15/29] add figures and change format

---
 vignettes/MalariaIndicator.Rmd                |   9 ++++++---
 .../figures/MI_IberianPeninsula_Wiki_2003.png | Bin 0 -> 3256845 bytes
 .../figures/MI_MapSuitability_2022-8.png      | Bin 0 -> 41609 bytes
 vignettes/figures/MI_TimeSeries_2022-8.png    | Bin 0 -> 37874 bytes
 4 files changed, 6 insertions(+), 3 deletions(-)
 create mode 100644 vignettes/figures/MI_IberianPeninsula_Wiki_2003.png
 create mode 100644 vignettes/figures/MI_MapSuitability_2022-8.png
 create mode 100644 vignettes/figures/MI_TimeSeries_2022-8.png

diff --git a/vignettes/MalariaIndicator.Rmd b/vignettes/MalariaIndicator.Rmd
index 2194069..626a4cf 100644
--- a/vignettes/MalariaIndicator.Rmd
+++ b/vignettes/MalariaIndicator.Rmd
@@ -271,6 +271,7 @@ source('https://earth.bsc.es/gitlab/es/s2dv/-/raw/master/R/PlotEquiMap.R')
 ColorBar <- s2dv::ColorBar
 clim.palette <- s2dv::clim.palette
 .IsColor<- s2dv:::.IsColor
+.SelectDevice <- s2dv:::.SelectDevice
 ```
 
 ```{r fig.width=7, fig.height=4}
@@ -288,7 +289,9 @@ PlotLayout(PlotEquiMap, c('lat', 'lon'),
            filled.oceans=TRUE,
            country.borders=TRUE,
            intylat=1,
-           intxlon=1)
+           intxlon=1,
+           fileout = "MI_MapSuitability_2022-8.png",
+           width = 7, height = 4)
 ```
 
 ### 4.2 Time series
@@ -301,7 +304,7 @@ function allows the user to compute a linear trend in the data.
 library(dplyr)
 library(ggplot2)
 
-data.frame(year=seq(year_in, year_fi, by=1),
+pt <- data.frame(year=seq(year_in, year_fi, by=1),
            ind=c(MeanDims(malariaInd$data, c('lon', 'lat'), na.rm=TRUE))) %>% 
   ggplot(aes(x=year, y=ind)) +
   geom_line() +
@@ -323,7 +326,7 @@ change
 The number of months suitable for malaria transmission appear to have increased 
 in the Iberian Peninsula between 2010 and 2020. When comparing the year 2020 with
 the year 2010, there was a `r change`% increase in the number of months suitable for
-malaria transmission in the region.
+malaria transmission.
 
 ## References
 
diff --git a/vignettes/figures/MI_IberianPeninsula_Wiki_2003.png b/vignettes/figures/MI_IberianPeninsula_Wiki_2003.png
new file mode 100644
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