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Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya
K J Linthicum1, A Anyamba, C J Tucker
1Department of Defense, Global Emerging Infections System, Division of Preventive Medicine, Walter Reed Army Institute of Research, Washington, DC 20307-5100, USA.
Science (New York, N.Y.)
|July 20, 1999
Summary
Predicting Rift Valley fever virus outbreaks in East Africa is possible up to five months in advance. This prediction uses rainfall data and satellite vegetation monitoring to identify high-risk areas and potential outbreak zones.
Area of Science:
- Epidemiology
- Environmental Science
- Remote Sensing
Background:
- Rift Valley fever virus (RVFV) outbreaks in East Africa historically correlate with periods of unusually high rainfall.
- Understanding the environmental precursors to RVFV outbreaks is crucial for public health preparedness.
Purpose of the Study:
- To investigate the potential for predicting RVFV outbreaks in East Africa.
- To establish a lead time for outbreak prediction using environmental and satellite data.
Main Methods:
- Analysis of historical RVFV outbreak records (1950-1998) in East Africa.
- Correlation of outbreak data with sea surface temperature anomalies in the Pacific and Indian Oceans.
- Utilizing satellite-derived Normalized Difference Vegetation Index (NDVI) data for monitoring.
Main Results:
- RVFV outbreaks consistently followed periods of abnormally high rainfall.
- Predictive models incorporating rainfall and sea surface temperature anomalies suggest a potential prediction window of up to five months.
- Near-real-time satellite NDVI data can identify currently affected areas.
Conclusions:
- RVFV outbreaks in East Africa can be predicted with significant lead time using environmental indicators.
- Satellite-based monitoring, particularly NDVI, offers a valuable tool for both prediction and real-time identification of outbreak zones.
- Early warning systems based on these findings can enhance public health response to RVFV.
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