Modeling the effects of weather and climate change on malaria transmission

Abstract

Background: In recent years, the impact of climate change on human health has attracted considerable attention; the effects on malaria have been of particular interest because of its disease burden and its transmission sensitivity to environmental conditions.

Objectives: We investigated and illustrated the role that dynamic process-based mathematical models can play in providing strategic insights into the effects of climate change on malaria transmission.

Methods: We evaluated a relatively simple model that permitted valuable and novel insights into the simultaneous effects of rainfall and temperature on mosquito population dynamics, malaria invasion, persistence and local seasonal extinction, and the impact of seasonality on transmission. We illustrated how large-scale climate simulations and infectious disease systems may be modeled and analyzed and how these methods may be applied to predicting changes in the basic reproduction number of malaria across Tanzania.

Results: We found extinction to be more strongly dependent on rainfall than on temperature and identified a temperature window of around 32-33 degrees C where endemic transmission and the rate of spread in disease-free regions is optimized. This window was the same for Plasmodium falciparum and P. vivax, but mosquito density played a stronger role in driving the rate of malaria spread than did the Plasmodium species. The results improved our understanding of how temperature shifts affect the global distribution of at-risk regions, as well as how rapidly malaria outbreaks take off within vulnerable populations.

Conclusions: Disease emergence, extinction, and transmission all depend strongly on climate. Mathematical models offer powerful tools for understanding geographic shifts in incidence as climate changes. Nonlinear dependences of transmission on climate necessitates consideration of both changing climate trends and variability across time scales of interest.

Figure 1

Fadeout probability and monthly temperature and rainfall averages in Tanzania. (A) Probability of local seasonal extinction for mosquitoes across Tanzania in April; darker areas indicate a higher probability of fadeout. (B) and (C) represent average temperature and rainfall values for Dar es Salaam, Singida, and Tanzania as a whole. Data from WorldClim (2009).

Figure 2

Effect of temperature and rainfall on mosquito population and Plasmodium species dynamics. (A) The mean number of mosquitoes per unit area as a function of temperature and rainfall. (B) Estimated doubling times of P. falciparum and P. vivax; high and low refer to vector density values: the number of mosquitoes per humans (M ÷ N). (C) The dependence of R₀ on temperature for P. falciparum and P. vivax.

Figure 3

Rainfall and temperature profiles and predicted R₀ changes in Tanzania. (A) Current rainfall and temperature profiles for Tanzania versus the predictions of HadCM3 for 2080 under A2a and B2a emission scenarios (data from WorldClim 2009). Predicted changes in R₀ across Tanzania in 2080 under (B) A2a and (C) B2a emission scenarios where ɛ ≈ 0.98 and ω ≈ 0.65 at present and ɛ ≈ 0.99 and ω ≈ 0.65 under A2a and B2a.