Predicting malaria dynamics under climate change

Malaria dynamics are closely tied to climate, as rainfed water pools provide the habitat for the Anopheles mosquitoes, and temperature influences this vector's ability to spread disease. Climate change drives shifts in microtopographic controls on the persistence of mosquito habitat and the life cycles of Anopheles vector and Plasmodium parasite, which affect the transmission of malaria. The ability to accurately predict malaria dynamics in the future requires the consideration of the impacts of modifications in ecohydrologic system under climate change on these shifts. The primary goal of this research is to investigate the relationships between the dynamics of malaria and changes in the ecohydrologic system due to the acclimation of vegetation under elevated atmospheric CO2 condition and temperature increase. We also aim to understand how the dominant controls of malaria interact under environmental perturbations by quantitatively analyzing changes in malaria incidence rates. Here, a coupled ecohydrology-malaria dynamics model is developed to predict malaria dynamics under projected climate change. The impacts of ecologic acclimation on soil moisture and persistence of ponded water that provide habitat for mosquitoes are captured using a coupled multi-layer canopy and physically-based flow surface-subsurface modeling approach. The transmission of malaria in response to these impacts and temperature increase are assessed by using a stochastic meta-popolation simulation model. We show that impacts of elevated CO2 and temperature have opposing effects on malaria prevalence. While air temperature increase shortens the life cycles of Anopheles and Plasmodium and increases the risk of spreading the disease, lower soil moisture resulting from increasing evapotranspiration reduces the habitat suitability for mosquitoes. The interplay between air temperature increases and soil moisture reduction under climate change leads to a smaller net increase in environmental suitability for malaria transmission than previously thought. In addition, we found larger net increase of malaria incidence under high temperature increase due to its nonlinear effects on the life cycles of vectors and parasites. The models and methods used are generalized and can be applied to other mosquito-borne diseases