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
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
. Between 2001 and 2009, the global malaria burden increased by over 34 million cases (~18%)
. According to the Malaria Atlas project
, global malaria incidence in 2007 was approximately 451 million cases (95% CI: 349,553)
. This estimate is 1.6-2.6 times higher than the total of ~200 million “suspected” cases reported by WHO for the same year
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
 and, in the early 1970s, WHO abandoned malaria eradication as “impracticable”
. 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)
The role of the Anopheles vector in malaria transmission has been appreciated since Ross
, 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. 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 CO2 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 (R0), and led to the conclusion that malaria would potentially increase globally and be re-introduced in countries such as Australia, the USA, and Europe
More recently, Ermert et al. 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 0.5o 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”
As Ermert et al. demonstrate
, output from “coarse global climate models” is inadequate for modelling the future of malaria. Hay et al. agree
, 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
. 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
. Moreover, climate variability (short-term fluctuations around the mean climate state) may be “epidemiologically more relevant” than long term mean temperature change
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
. 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
 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. 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
, making a variety of disease and population models, models for interventions, and tools for fitting models to reference data available to any researcher
. Source code, executable binaries, and reference documentation are available under the Eclipse Public License (EPL)
. 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.