Climate and Malaria in Africa

Economic development has played an enormous role in shaping the current global distribution of malaria. Where malaria is not adequately controlled, however, its distribution and seasonality are closely related to seasonal characteristics of the climate. [Full Discussion]

The mapping products below therefore aim to illustrate models of climate suitability for seasonal endemic malaria, and recent climate conditions, such as rainfall anomalies, which may be associated with epidemic malaria in warm semi-arid regions of Africa. Additional models, such as that developed by the MARA Initiative, may be included in the future and we welcome the opportunity to work with others on the further development of these products.

Monitoring Tools for Epidemic Malaria
Malaria Early Warning System
MEWS is a rainfall-monitoring product based on dekadal rainfall estimates from the Climate Prediction Center. The interface allows users to view recent rainfall estimates with a seasonal and recent historical perspective. Time series analyses of rainfall data are generated based on user-selected parameters.
Rainfall Estimate Differences
The Rainfall Estimate Differences (RED) map illustrates the difference between the most recent dekadal rainfall estimates from the Climate Prediction Center and their short term average (from 2000 to last recent complete year). These differences should not be confused with conventional rainfall anomalies, but may provide insight into changes in malaria risk in areas where precipitation anomalies are the principal cause of malaria epidemics by providing a recent historical reference.
Rainfall Estimate Percentages
The Rainfall Estimate Percentages (REP) map expresses the most recent dekadal rainfall estimates from the Climate Prediction Center as a percentage of the short term average (from 2000 to last recent complete year).
MODIS Image Download Tools
Several regional tools facilitate access to MODIS images, which are provided by the United States Geological Survey. Images are available for West Africa, East Africa, and Southern Africa.
NDVI Analysis Tool
An interactive map of the Normalized Difference Vegetation Index for West Africa, East Africa, and Southern Africa. Time series analyses of NDVI are generated based on user-selected parameters.
EVI Analysis Tool
An interactive map of the Enhanced Vegetation Index for West Africa, East Africa, and Southern Africa. Time series analyses of EVI are generated based on user-selected parameters.
Country-Average WASP Index
A tool for displaying time series of country-averages of the WASP precipitation index with respect to a user-selected reference year.
Informational Tools for Endemic Malaria
Seasonal Climatic Suitability for Malaria Transmission*
Empirically-derived thresholds of precipitation, temperature and relative humidity are used to assess the climatic suitability of malaria transmission. The interactive map initially displays the number of months during the year when climatological averages meet these requirements. Users may gain insight into how often these conditions have actually occurred during any particular month by clicking on the map at the location of interest.
MARA Distribution Model of Climatic Suitability for Malaria Transmission*
The MARA distribution model is a static map illustrating climatic suitability for malaria transmission. Climatological averages of precipitation and temperature are utilized in a fuzzy logic model developed by scientists associated with the Mapping Malaria Risk in Africa (MARA) group (after Craig et al. 1999). More information about the MARA collaboration is available here.
*Please note that these maps are intended for informational purposes only. They are based on a theoretical models of climate data and are not based on actual malaria data. Therefore, they may not accurately resemble actual malaria transmission status on a local scale.
Malaria Atlas Project (MAP) Maps of Malaria Transmission and Endemicity
The Malaria Atlas Project (MAP) has constructed a global database and atlas of maps that show the spatial limits of Plasmodium falciparum transmission based upon parasite rate surveys, the predicted spatial distribution of Plasmodium falciparum malaria endemicity, and the model uncertainty for the predicted endemicity. Further details about the creation of these maps can be found in Hay et al. 2009 and at the MAP website (external link).
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