Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa, Kenya

 Nanyingi M O1,3, Muchemi G M1, Kiama S G2,Thumbi S M5,6 and Bett B4


1Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX PO BOX 29053-0065 Nairobi, Kenya 2 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of Nairobi PO BOX 30197 Nairobi, Kenya 3 Colorado State University, Livestock-Climate Change Collaborative Research Support Program, CO 80523-1644,USA 4International Livestock Research Institute (ILRI), Naivasha Road, P.O. Box 30709 Nairobi 00100, Kenya 5Kenya Medical Research Institute, US Centres for Disease Control and Prevention, PO Box 1578 Kisumu 6 Paul G Allen Global Animal Health, PO Box 647090, Washington State University, Pullman WA 99164-7090,509-335-2489

Abstract

Rift Valley Fever (RVF) is an arthropod-borne viral zoonosis with a potential global threat to domestic animals and humans. Climate variability is recognized as one of the major drivers contributing RVF epidemics and epizootics that have been closely linked to cyclic occurrence of the warm phase of the El Niño southern oscillation (ENSO) phenomenon. Using retrospective reanalysis and cross sectional participatory approaches we evaluate the impacts of climate change on pastoral communities and outline their roles in community based early warning systems for RVF. We compare the spatiotemporal correlation of normalized difference vegetation index (NDVI) and Rainfall Estimate Differences as surrogate predictors of RVF outbreaks in Garissa over the past decade. A bivariate regression model to provide a month-ahead lead-time for earlier prediction of RVF is described. We also explore the recent RVF outbreaks linkage to other environmental conditions using long-term sentinel data collected on the field. The results indicate a significant correlation between elevated rainfall and NDVI (> 0.43) anomalies with recent RVF epidemics (P < 0.5). Persistent elevated rainfall and NDVI suggest that there is a likelihood of another RVF outbreak due to enhance vector competence. Given the nearly linear relationship between rainfall and NDVI it is thus possible to utilize these factors to examine and predict spatially and temporally RVF epidemics for effective surveillance with limited resources. This small-scale focal study will contribute to various existing predictive tools and present a good opportunity for preparedness and mitigation of RVF by local, national and international organizations involved in the prevention and control of RVF.

Key words: Rift Valley Fever, Climate change; Early Warning Systems; Garissa