Modelling Influence of Climate Change on Global Malaria Distribution

by Amelia Hamiter

Malaria is an infectious disease which has significantly affected global human populations throughout history. Its impact declined greatly throughout the 20th century in many regions due to extensive intervention efforts, though it continues to be found in tropical areas such as parts of Africa. Like other vector-borne diseases, though, the distribution and “seasonal activity” of malaria could potentially be altered by global climate change. One way to prepare for changes in patterns of malaria outbreaks is to model scenarios of change under different climate outcomes.

Fortunately, malaria is one of the few climate-sensitive health events for which more than one research group have done such a study. Since a variety of malaria models of both global and regional scale exist, it is possible to do a “multimodel intercomparison,” which results in a more thorough projection of potential climate change effects. This study is the first to perform a multimodel intercomparison, concluding that the most significant impacts of climate change are likely to happen in the highlands in Africa, and in parts of South America and Southeast Asia, and less likely to happen in more developed regions, most likely because of the socioeconomic factors which enabled these regions to reduce malaria in the 20th century, such as strong public health services. Because the malaria models left out consideration of factors such as human migration and urbanization, technological advances, economic developments, change in land use, changes in vector and disease control, and adaption and evolution of the malaria parasite and vector species, they are dearly uncertain, as the authors note. They also did not account for how other effects of climate change (such as social, economic, and political changes) will have an impact on how human populations of the future will be able to manage disease outbreaks. Considering that human interventions had a significant impact on malaria distribution in the 20th century, it is important to look at such factors in a malaria model. The researchers add that as future biological models become more complex, the differences between them will grow, making it worth questioning whether it would be better to stick to the most simple models instead. They also advise doing further malaria model work at regional or national scales to glean a more accurate picture of populations at risk. The authors conclude that while climate-sensitive alterations in malaria distribution are important, it is “unlikely” that they will be a major player in the net disease outcomes of climate change.

This study was done as a part of the Inter-Sectoral Impact Model Intercomparison Project, a collaboration that develops methods of intercomparison in order to better understanding of impact models. The researchers used five malaria models: LMM_RO, MIASMA, VECTRI, UMEA, and MARA, all of which were developed for different regions and with different objectives, and had different parameters and designs. This intercomparison used climate outputs from five global climate models (CGMs), running each under four different emissions scenarios (these were Representative Concentration Pathways (RCPs) developed for the fifth Intergovernmental Panel on Climate Change (IPCC) assessment report). Using these tools and a single population projection, researchers evaluated climate suitability – how the climate may become more suitable for malaria transmission – and populations at risk. Their findings indicated net increase in both, but with large uncertainty due to the fact that the malaria models did not include possible effects of many factors affecting malaria distribution besides climate change.

Caminade, C., Kovats, S., Rocklov, J., Tompkins, A.M., et al., 2014. Impact of climate change on global malaria distribution. PNAS 111, 3286-3291.

Caminade, C., Kovats, S., Rocklov, J., Tompkins, A.M., et al., 2014. Impact of climate change on global malaria distribution. PNAS 111, 3286-3291.

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