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An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

DOI: 10.1186/1475-2875-4-6

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Abstract:

Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.It is estimated that more than 110 million Africans live in areas prone to epidemics of malaria. Populations in these areas are infrequently challenged by malaria and, therefore, do not fully develop acquired immunity. As a result, the disease remains life threatening to all age groups. The impact of malaria epidemics could be greatly reduced by timely detection or, ideally, by prediction and prevention through

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