Background
Malaria remains a major health problem in much of the tropics and subtropics. The World Health Organization (WHO) estimates that there were 225 million cases of malaria in 2009 and more than 780,000 deaths from the infection in 2010[
1,
2]. The malaria parasite is transmitted from human to human primarily by the bite of the
Anopheles mosquito[
3‐
5].
In 2006,
Plasmodium falciparum accounted for 92% of infections globally and for 98% in Africa, a continent that had 91% of the global deaths that year[
1]. Between 2001 and 2009, the global malaria burden increased by over 34 million cases (~18%)[
2]. According to the Malaria Atlas project[
6], global malaria incidence in 2007 was approximately 451 million cases (95% CI: 349,553)[
6]. This estimate is 1.6-2.6 times higher than the total of ~200 million “suspected” cases reported by WHO for the same year[
2].
Control efforts begun in the 1940s “virtually eliminated” malaria transmission in parts of the Americas, Europe, and Asia, but “largely bypassed” the African tropics where the intensity of transmission was much higher[
7,
8]. DDT-resistance appeared in mosquito vector species, decreasing its effectiveness in indoor residual spraying[
8] and, in the early 1970s, WHO abandoned malaria eradication as “impracticable”[
9]. The malaria parasite has also developed resistance to front-line drugs, notably to chloroquine (its effectiveness compromised by extensive and widespread use) and, more recently, to artemisinin (used in combination therapy as a replacement to chloroquine)[
10,
11].
The role of the
Anopheles vector in malaria transmission has been appreciated since Ross[
3], and multiple studies[
12‐
19] have established the effect of climate on
Anopheles populations. A number of groups have used numerical simulation and modelling in an attempt to prioritize and inform intervention and control efforts[
12‐
21]. The US Geological Survey (USGS) and others have developed numeric models to inform public health officials of non-endemic regions likely to experience an increase in vector capacity based on climate change[
13‐
20]. Martens
et al.[
12] asked “if other things were held constant in the world, what would be the impact of climate change
per se on the distribution of malaria?” They applied two general circulation models (GCM), assuming a doubling of the atmosphere CO
2 levels by 2050 (the models were UKMO-GCM and ECHAM1-A-GCM). Their approach established a relationship between environmental factors (temperature and precipitation) and the parasite’s reproductive number (R
0), and led to the conclusion that malaria would potentially increase globally and be re-introduced in countries such as Australia, the USA, and Europe[
12].
More recently, Ermert
et al.[
22] asked whether “potential weather-driven changes” would affect malaria transmission. They carried out projections using a high-resolution regional climate model (RCM) data set that included greenhouse-gas and land-use and land-cover (LUC) changes in a regional model (REMO). Their approach integrated bias-corrected temperature and precipitation data with the Liverpool Malaria Model at a 0.5
o latitude-longitude grid. The higher spatial resolution of the RCM allowed them to capture the effects of local terrain on temperature and rainfall and to account for future changes in land characteristics (eg, diminished vegetation due to human activities). They concluded that climate change will significantly affect the geographic distribution of malaria in tropical Africa “well before 2050”[
22].
As Ermert
et al. demonstrate[
22], output from “coarse global climate models” is inadequate for modelling the future of malaria. Hay
et al. agree[
23], noting that, while dependent on climate factors, “malaria does not respond to approximated averages.” While satellite climate data is available with high resolution, the malaria surveillance data required to calibrate models is often available only as a country-wide spatial average; reporting is often based on monthly or even yearly totals. Uncertainty in absolute reporting fraction and absolute disease incidence makes model calibration problematic. Even with long-term systematic changes to the earth’s climate, predicting malaria risk for
specific locales and regions is difficult[
23]. Malaria may spread to newly emergent regions only when local conditions are favourable, and recede in areas when conditions are unfavourable to the malaria protozoa or the
Anopheles mosquito vector[
12]. Moreover, climate
variability (short-term fluctuations around the mean climate state) may be “epidemiologically more relevant” than long term mean temperature change[
24].
To evaluate how changing environmental factors affect the malaria burden, this study uses a response function as a measure of malaria sensitivity to fluctuations in climate. The measure is inspired by the thermodynamic “susceptibility” as defined in physics, namely the
response of a substance, or material property, to an applied field[
25]. In this case, the focus is on the response of malaria incidence to fluctuations in climate variables. Given the demonstrated effect of vector capacity on the effective reproductive number for malaria transmission, the response function is computed based on fluctuations in land surface temperature and land precipitation. Evaluation of sensitivity to other dependent variables is possible (and left to future work).
The approach is to explore and test measures based on
relative differences in reported malaria incidence; measures that would not depend on absolute calibration. While available public health data may be based only on national averages or monthly reporting, numerical models can be evaluated and compared at varying levels of spatiotemporal resolution. The statistical bootstrap method[
26] is used to measure the uncertainty in predicted means as a function of spatial resolution based on surveillance data and modelling to learn how improving resolution might affect uncertainty.
The current study makes no attempt to predict future climate change. Rather, it simply asks, “given the historic variation in global climate in the years 2001 to 2010, how did malaria potential increase or decrease by local geographic region in those years?” It then uses historic WHO data, and model predictions, to measure the “sensitivity” of malaria incidence to actual changes in temperature and precipitation. In principle, this approach would allow researchers to evaluate the response to any variable believed to influence vector capacity.
Many environmental factors[
16‐
23] influence the sporogonic cycle of
Anopheles[
4]. To take these factors into account in estimating regional malaria transmission, this study constructs a composite model of malaria using an
Anopheles vector capacity model as input to a Macdonald Ross malaria model[
3‐
5,
20,
21]. The underlying vector capacity model is based upon a function of earth science data. The earth science data includes global land elevation from the National Oceanic and Atmospheric Administration (NOAA), land surface temperature at night from the National Aeronautics and Space Administration (NASA), and historic precipitation and Normalized Difference Vegetation Index (NDVI) from NASA Earth Observatory (NEO)[
20,
21,
27‐
30].
All models and all denominator data used here are freely available as open source through the Spatiotemporal Epidemiological Modeller project (STEM)[
31,
32]. As an Eclipse Foundation project, STEM supports community collaboration[
33], making a variety of disease and population models, models for interventions, and tools for fitting models to reference data available to any researcher[
34]. Source code, executable binaries, and reference documentation are available under the Eclipse Public License (EPL)[
35]. In addition to the extended MacDonald Ross Model, STEM has stochastic and deterministic models for a wide variety of infectious, vector borne, food-borne, and zoonotic diseases. All STEM models, including those described here, may be freely used, modified, extended, and distributed; details of the current model are available on Eclipsepedia[
36,
37]. The response function analysis is independent of any particular model and is also evaluated based exclusively on surveillance data.
Conclusions
This paper reports the use of an open source tool, the Spatiotemporal Epidemiological Modeller, to create a global malaria model built on top of a global model of the Anopheles vector. Results of the simulation are compared with national malaria estimates from WHO and the Malaria Atlas project. Calibration of absolute incidence is problematic as official estimates of malaria burden are available only at the national level whereas accurate modelling requires data at higher spatial resolution.
To overcome this difficulty, this study explores new measures of malaria
response to fluctuations in key climate variables. The measures can be applied to both simulation and surveillance data at different spatial and temporal resolutions to identify those locations where malaria is most sensitive to variation in temperature, precipitation, or other climate variables. In the future it is certainly desirable to measure sensitivity to other variables known to influence malaria transmission including relative humidity, hours of daylight, vector control efforts, etc.[
16‐
23]. In some regions these other factors may even dominate local changes in malaria burden. Malaria response requires coordinated global policies. The high spatial resolution possible with state-of-the-art numerical models can inform public health and identify those regions most likely to require intervention in a given year based on variations in weather and climate.
Bootstrapping analysis finds a potential 20x improvement in accuracy if data were available at the level ISO 3166–2 national subdivision and with monthly time sampling. When limited to data at the national level, knowledge of country average sensitivity of malaria to changes in precipitation and temperature allows one to predict whether malaria burden will increase or decrease (given accurate climate data) with approximately 70-75% confidence. The sensitivity analysis should become more accurate by including the response to other important factors (eg, relative humidity), known country level intervention efforts, and by increasing the spatial resolution of malaria surveillance allowing measurement of sensitivity to climate on smaller spatial scales.
Surveillance data with this resolution would also support more accurate calibration of predictive models of malaria burden. In such endeavours, the availability of an open-source modelling framework, such as STEM, would allow diverse communities of scientists to build on the tools and data it provides, incrementally re-using, refining, and extending its capabilities. The model itself can be improved over time as the historic climate and historic malaria data sets improve. New denominator data can be added reflecting actual malaria interventions and mosquito vector control efforts by country.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
SE is project co-lead for the spatiotemporal epidemiological modeller; he developed the models, ran the simulations, and contributed to data analysis. MD assisted with model design, GIS data processing and data analysis. JVD performed literature reviews and assisted in writing and editing of the manuscript. AK advised contributed to the vector population model and the extended MacDonald Ross Model. NW conducted a preliminary review of the vector capacity scientific literature and helped develop the vector population model. JL reviewed the research design and advised on statistical analytical techniques. JHK directed the research, led the data analysis, and wrote the final paper. SE and JL performed critical review of the final manuscript. All authors read and approved the final manuscript.