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Real-time monitoring and forecasting of Rift Valley fever in Africa

29/07/2019
Keywords: Climate, Early warning, Early warning early action, Eastern Africa, EMPRES, Ethiopia, Flood, Food chain threats, Food security, Gambia, Good practices, Horn of Africa, Kenya, Livestock, Mauritania, Middle East, Rwanda, Senegal, South Africa, Southern Africa, Sudan, Western Africa

Rift Valley fever (RVF) is a vector-borne disease that has severe impacts on livelihoods, national and international markets, and human health. RVF is currently limited to Africa and parts of the Near East; however, it is recognized to have the potential to expand globally. In livestock, the disease is spread primarily by mosquitoes and the movement of animals. The clinical disease, which is zoonotic, has been observed in sheep, goats, cattle, buffaloes, camels and humans. RVF can result in widespread febrile illness in humans, and may be associated with severe and sometimes fatal sequelae in under 1 percent of cases.

Outbreaks of RVF are closely associated with climate anomalies, such as periods of heavy rains and prolonged flooding, which increase habitat suitability for vector populations and thus influence the risk of disease emergence, transmission and spread. In this context, early warning systems represent an essential tool, as they provide information on occurring animal health hazards that might evolve into disasters unless an early response is undertaken.

To enable national authorities to implement measures preventing outbreaks, Food and Agriculture Organization of the United Nations (FAO) has developed the RVF Monitoring/Early Warning System. This tool has been crucial in successfully forecasting hotspots for RVF vector amplification, as it provides recommendations and early warning messages for countries at risk of RVF outbreaks. This information sheet describes the innovative work done by FAO, consisting in the implementation of the RVF climate-based model in the Google Earth Engine (GEE) platform and the development of an early warning tool that enables near-real-time monitoring and forecasting of RVF at-risk areas in Africa, with a spatial resolution of 250 m.

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