Rift Valley fever epidemiology, surveillance, and control: what have models contributed?

Métras, Rëlle.; Collins, L.M.; White, R.G.; Alonso, S.; Chevalier, Véronique.; Thuranira-McKeever, C.; Pfeiffer, D.U.

Vector Borne and Zoonotic Diseases 11(6): 761-771


ISSN/ISBN: 1530-3667
PMID: 21548763
DOI: 10.1089/vbz.2010.0200
Accession: 055573875

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Rift Valley fever (RVF) is an emerging vector-borne zoonotic disease that represents a threat to human health, animal health, and livestock production, particularly in Africa. The epidemiology of RVF is not well understood, so that forecasting RVF outbreaks and carrying out efficient and timely control measures remains a challenge. Various epidemiological modeling tools have been used to increase knowledge on RVF epidemiology and to inform disease management policies. This narrative review gives an overview of modeling tools used to date to measure or model RVF risk in animals, and presents how they have contributed to increasing our understanding of RVF occurrence or informed RVF surveillance and control strategies. Systematic literature searches were performed in PubMed and ISI Web of Knowledge. Additional research work was identified from other sources. Literature was scarce. Research work was highly heterogeneous in methodology, level of complexity, geographic scale of approach, and geographical area of study. Gaps in knowledge and data were frequent, and uncertainty was not always explored. Spatial approaches were the most commonly utilized techniques and have been used at both local and continental scales, the latter leading to the implementation of an early warning system. Three articles using dynamic transmission models explored the potential of RVF endemicity. Risk factor studies identified water-related environmental risk factors associated with RVF occurrence in domestic livestock. Risk assessments identified importation of infected animals, contaminated products, or infected vectors as key risk pathways for the introduction of RVF virus into disease-free areas. Enhanced outbreak prediction and control and increased knowledge on RVF epidemiology would benefit from additional field data, continued development, and refinement of modeling techniques for exploring plausible disease transmission mechanisms and the impact of intervention strategies.