Impact of climate change on global malaria distribution

Abstract

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.

Keywords: disease modeling; global climate impacts; uncertainty.

Fig. 1.

Observed (A and B) and simulated malaria distribution (three categories: risk-free in white, unstable/epidemic in blue, and stable/endemic in red) for five malaria models (C, D, E, F and G). For the observation (A and B) all endemic subcategories (hypoendemic, mesoendemic, hyperendemic, and holoendemic) have been included in the stable category. The 1900s data (A) are based on ref. (considers all plasmodium infections), and the 2000s data (B) are based on ref. (considers only P. falciparum infections). For the simulations, unstable malaria is defined for a length of the transmission season (LTS) ranging between 1 and 3 mo, and suitable is defined for LTS above 3 mo (based on TRMMERAI control runs for the period 1999–2010; SI Appendix, Fig. S11 shows the CRUTS3.1 control runs). The TRMMERAI runs are constrained to span 50°N–50°S owing to the TRMM satellite data availability. For the UMEA malaria model only estimates of stable malaria were available.

Fig. 2.

The effect of climate scenarios on future malaria distribution: changes in LTS. Each map shows the results for a different emission scenario (RCP). The different hues represent change in LTS between 2069–2099 and 1980–2010 for the ensemble mean of the CMIP5 subensemble. The different saturations represent signal-to-noise ratio (μ/Sigma) across the super ensemble (the noise is defined as one SD within the multi-GCM and multimalaria ensemble). The hatched area shows the multimalaria multi-GCM agreement (60% of the models agree on the sign of changes if the simulated absolute changes are above 1 mo of malaria transmission).

Fig. 3.

The estimated population at risk owing to climate change impacts on malaria distribution for three time periods for four selected regions in Africa. (A) Additional population at risk for malaria owing to climate change based on the MARA, MIASMA, LMM, VECTRI, and UMEA models. (B) Additional person-months at risk for malaria based on the MARA, MIASMA, LMM, and VECTRI models.