|Linthicum, Kenneth - Ken|
Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 7/27/2007
Publication Date: 11/4/2007
Citation: Anyamba, A., Chretien, J., Small, J., Tucker, C., Formenty, P., Richardson, J., Britch, S.C., Linthicum, K. 2007. Forecasting the Temporal and Spatial Distribution of a Rift Valley fever Outbreak in East Africa: 2006-2007. American Society of Tropical Medicine and Hygiene 2007 Conference in Philadelphia, PA on November 4-8, 2007, pgs. 282-283. Interpretive Summary: N/A
Technical Abstract: El Niño/Southern Oscillation (ENSO) related climate anomalies have been shown to have a direct impact on Rift Valley fever (RVF) disease outbreaks. Knowledge of the links between ENSO driven climate anomalies and RVF can allow us to provide improved long range forecasts of an epidemic or epizootic. We used satellite generated data to detect that sea surface temperatures (SSTs) in the equatorial east Pacific ocean anomalously increased significantly during July – October 2006 indicating the typical development of El Niño conditions. The persistence of these conditions and the concurrent elevation of the SSTs in the Indian Ocean was similar to extremes in global-scale climate anomalies that have been observed during similar conditions in the past that produced excess rainfall in East Africa. Subsequent normalized difference vegetation index (NDVI) anomalies for Africa showed positive NDVI patterns with the largest departures concentrated over East Africa especially eastern Kenya, Somalia southern Ethiopia and northern Tanzania following above normal rainfall from September through December. A RVF risk map derived from thresholding NDVI anomaly data indicated for the period October to December 2006 that there was elevated risk of RVF activity in northern Kenya, central Somalia, and subsequently Tanzania. An outbreak of RVF occurred in northeastern Kenya in early December and has continued in east Africa through April 2007. We describe the spatial and temporal accuracy of our RVF risk map forecasts. Forecasting disease is critical for timely and efficient planning of operational control programs. In this paper we describe how we can refine our RVF risk model to give decision makers additional tools to make rational judgments concerning implementation of disease prevention and mitigation strategies.