title: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Department of Infectious Disease Epidemiology, Grantham Institute for Climate Change, Imperial College London, London, United Kingdom. paul.parham@imperial.ac.uk
authors.forename: Paul Edward
authors.initials: PE
authors.lastname: Parham
authors.forename: Edwin
authors.initials: E
authors.lastname: Michael
mesh_terms: D000818:Animals; D000852:Anopheles / Q000469:parasitology; D057231:Climate Change; D004782:Environmental Health; D006801:Humans; D007303:Insect Vectors / Q000469:parasitology; D008288:Malaria / Q000635:transmission*; D016778:Malaria, Falciparum / Q000635:transmission; D016780:Malaria, Vivax / Q000635:transmission; D008954:Models, Biological; D011157:Population Dynamics; D012621:Seasons; D013636:Tanzania; D014887:Weather