Most malaria endemic countries experience seasonal variation in transmission. Georeferenced time-series data from peer-reviewed articles and routine case reporting are collated and an inferential model constructed to relate transmission data to a suite of temporally dynamic environmental covariates (temperature, vegetation, humidity etc.) from the MODIS remote sensing platform.
The initial aim of this work is to be able to accurately predict seasonal malaria transmission patterns (onset, duration, magnitude) in locations where malaria survey data are sparse, using only the environmental covariate data which are available for all locations. The results from this endeavour will then be used to help model malaria prevalence an incidence at sub-annual scales such as monthly.
We are gathering data on seasonality to support this research.