A weather-driven model of malaria transmission.
BACKGROUND
Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission.


METHODS
This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages.


RESULTS
Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection.


CONCLUSIONS
A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts.
Hoshen
Moshe B
MB
Virtual Population Laboratory, Department of Physics, University of Liverpool, Liverpool L69 7ZE, UK. hoshen@liv.ac.uk
Morse
Andrew P
AP
D000349:Africa; D000818:Animals; D000852:Anopheles / Q000254:growth & development / Q000469:parasitology / Q000502:physiology*; D002417:Cattle; D002980:Climate; D005260:Female; D006790:Host-Parasite Interactions; D006801:Humans; D007303:Insect Vectors / Q000254:growth & development / Q000469:parasitology / Q000502:physiology*; D016778:Malaria, Falciparum / Q000635:transmission*; D008954:Models, Biological; D011157:Population Dynamics; D011336:Probability; D011891:Rain; D012621:Seasons; D013045:Species Specificity; D013269:Stochastic Processes; D013696:Temperature; D014887:Weather; D015030:Zimbabwe