291 - A revised model to predict Rift Valley fever virus transmission risk for livestock

Abstract: Rift Valley fever (RVF) is a mosquito-borne viral hemorrhagic zoonosis largely confined to Africa and the Arabian Peninsula, which poses significant threats to public health and the agricultural economy. Rift Valley fever virus (RVFV) is transmitted to ungulate livestock primarily through the bite of infectious Aedes and Culex spp. mosquito vectors, and subsequently causes extensive livestock death. Humans can become infected via handling or consumption of fluids or tissue of infectious livestock. Accurate forecasting of RVFV livestock transmission risk is crucial for mobilizing timely interventions to mitigate impacts to local livestock populations and potential transmission to other regions. We previously implemented a threshold RVFV transmission model based on satellite derived normalized difference vegetation index (NDVI) data that has been deployed for use for over a decade. Recently, to improve both spatial and temporal accuracy of transmission risk, multiple machine learning classification models were developed utilizing a comprehensive suite of bioclimatic variables including: satellite-derived NDVI and rainfall datasets, human population and livestock distributions, soils and hydrologic data, and records of historical RVFV livestock cases dating back to 2000. Recognizing that RVF outbreaks have been associated with El Niño–Southern Oscillation (ENSO) events across Africa, our model considers the unique teleconnection patterns of ENSO (El Niño and La Niña) across three regions of Africa (Southern Africa, Eastern Africa, and the Sahel). Machine learning model performances were compared via accuracy and ROC curves generated with an independent test set of livestock case data. All models have accuracy scores of 80% or greater. Validation against historical livestock case data demonstrates the model’s capability to identify high-risk periods and regions with improved precision and lead time, increasing the time available for health officials to implement mitigation measures in a more precise location.