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Rift Valley fever Risk Mapping and Prediction: Progress, Challenges and Future Opportunities.

Assaf Anyamba1, 2*
  • 1 Universities Space Research Association (USRA), United States
  • 2 Goddard Space Flight Center, National Aeronautics and Space Administration, United States

Rift Valley fever (RVF) is an acute viral disease that affects humans and domestic animals (such as cattle, buffalo, sheep, goats and camels). It is endemic in Sub-Saharan Africa and the Saudi Arabian Peninsula. The virus has potential for globalization and its outbreaks in the endemic region have substantially impacted public health, trade and economy – costing about US$17.8. Million in South Africa (during the 2010 epizootic) and $60M for the East Africa countries during the 2006-2007 outbreak. Its cross-over characteristics make it significant as a potential biological threat agent requiring focused understanding of outbreaks in the primary epizootic/epidemic regions. Over the last 20 years substantial progress has been made in risk mapping and prediction by leveraging satellite data sets (normalized difference vegetation index, rainfall, sea surface temperatures) and understanding linkages episodic outbreaks of Rift Valley fever with global patterns of interannual climate variability associated with the El Niño-Southern Oscillation (ENSO) phenomena. This capability has enabled early warning leads time of 2-4 months for various regions that area impacted to varying success in control and prevention. However, challenges remain including lack of specificity in risk characterization across different regions that reflect the varying ecosystems, microclimate, trade practice and vector species population dynamics. In addition, coordinated regional prevention and control efforts are challenging as one country without an outbreak does not feel the necessity for preventive measures despite the transboundary characteristics of Rift Valley fever. Critically, the absence of high quality livestock distribution/density maps and immunity status in livestock population are barriers to improved spatial specificity in risk mapping. The future holds promise for opportunities in improved prediction using seasonal climate forecast information combined with machine learning methods. This will provide longer lead times for prevention efforts to be undertaken. Importantly international collaborations efforts among stake holders including Food and Agriculture Organization of the United Nations, World Organisation for Animal Health (OIE), World Health Organization (WHO), partner government and supporting agencies remain a critical component of an early warning framework to enable timely surveillance (of vectors, livestock) and prevention efforts.

Acknowledgements

The Rift Valley Monitor Project is supported by Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) (1999-present). Current research and developments efforts are supported by Department of Defense (DoD) Defense Threat Reduction Agency (DTRA) Biological Threat Reduction Program (BTRP) through the Rift Valley fever in South Africa Project.

References

Linthicum, K. J., A. Anyamba, C.J.Tucker, P.W.Kelley, M.F. Myers and C.J. Peters (1999) Climate and Satellite Indicators to Forecast Rift Valley Fever Epidemics in Kenya. Science. 285: (5426), 397-400. Anyamba, A., K. J. Linthicum, R. Mahoney and C.J. Tucker (2002), Mapping Potential Risk of Rift Valley fever outbreaks in African Savannas using Vegetation Index Time Series Data. Photogrammetric Engineering Remote Sensing: Special Issue – Remote Sensing and Human Health. 68(2): 137-145. Anyamba, A., Chretien, J., Small, J., Tucker, C.J., Formenty, P., Richardson, J.H., Britch, S.C., Schnabel, D.C., Erickson, R.L., Linthicum, K. (2009). Prediction of a Rift Valley fever Outbreak. Proceedings of the National Academy of Sciences. 106(3): 955-959. http://www.pnas.org/content/106/3/955.full.pdf+html Anyamba, A., K. J. Linthicum, J. Small, S. C. Britch, E. Pak, S. de La Rocque, P. Formenty, A. W. Hightower, R. F. Breiman, J.P. Chretien, C. J. Tucker, D. Schnabel, R. Sang, K. Haagsma, M. Latham, H. B. Lewandowski, S. O. Magdi, M. A. Mohamed, P. M. Nguku, J.M. Reynes, and R. Swanepoel (2010) Prediction, Assessment of the Rift Valley Fever Activity in East and Southern Africa 2006-2008 and Possible Vector Control Strategies. Am J Trop Med Hyg. 83: 43-51. K. J. Linthicum, S. C. Britch, A. Anyamba (2016) Rift Valley Fever: An Emerging Mosquito-Borne Disease. Annu. Rev. Entomol. 61:395–415. doi: 10.1146/annurev-ento-010715-023819 Anyamba, A., Chretien, J-P., Britch,S.C., Soebiyanto, R.P., Small, J. L., Jepsen, P., Forshey, B.M., Sanchez, J.L., Smith, R.D., Harris, R., Tucker, C.J., Karesh, W.B., Linthicum, K.J. (2019). Global Disease Outbreaks Associated with the 2015-2016 El Niño Event. Nature Scientific Reports. 9:1930 | https://doi.org/10.1038/s41598-018-38034-z

Keywords: Rift Valley Fever, El Nino, NDVI (Normalized Difference Vegetation Index), outbreak, Sea Surafce Temperature

Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.

Presentation Type: Keynote

Topic: Emerging GIS, data science and sensor technologies adapted to animal, plant and human health, including precision medicine and precision farming

Citation: Anyamba A (2019). Rift Valley fever Risk Mapping and Prediction: Progress, Challenges and Future Opportunities.. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00004

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Received: 31 Jul 2019; Published Online: 27 Sep 2019.

* Correspondence: Dr. Assaf Anyamba, Universities Space Research Association (USRA), Columbia, United States, anyambaa@ornl.gov