MAP uses epidemiological data and geostatistical modeling approaches to produce maps and estimates of malaria burden.
Georeferenced time-series data from peer-reviewed articles and routine case reporting are collated and an inferential model is used to relate transmission data to a suite of temporally dynamic environmental covariates (temperature, vegetation, humidity etc.) from the MODIS remote sensing platform.
Most malaria affected 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 ultimate aim is to be able to accurately predict seasonal malaria transmission patterns (onset, duration, magnitude) in locations where malaria survey data is sparse, using only the environmental covariate data which is available for all locations.
# Improved intervention coverage models
Three major interventions used globally are insecticide treated nets (ITN), artemisinin-based combination therapy (ACT) and indoor residual spraying (IRS). Earlier work by MAP has generated detailed models of ITN, ACT and IRS coverage in parallel to spatiotemporal reconstructions of changing endemicity through time in Africa. MAP is working to extend the African ITN model to triangulate data from net manufacturers, national malaria control programmes and household surveys into coverage models. This will allow definitive evaluation of ITN coverage and enable future ITN distribution can be more targeted and risk driven. Similar models will be produced for ACTs, IRS, and (where regionally appropriate) intermittent preventive treatment in pregnancy (IPTp) and seasonal malaria chemoprevention (SMC).