Malaria Suitability Indicator

Earth Sciences department, Barcelona Supercomputing Center (BSC)

2022-08-30

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

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

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

# 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

3. Compute indicator

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

# 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

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$data[1,1,c(1,dim(malariaInd$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.

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