Present and Future Ecological Niche Modeling of Rift Valley fever in East Africa in Response to Climate Change

Abstract

Rift Valley fever (RVF) has been linked with recurrent outbreaks among humans and livestock in several parts of the globe. Predicting RVF's habitat suitability under different climate scenarios offers vital information for developing informed management schemes. The present study evaluated the probable impacts of climate change on the distribution of RVF disease in East Africa (E. A.), using the maximum entropy (MaxEnt) model and the disease outbreak cases. Considering the potential of the spread of the disease in the East Africa region, we utilized two representative concentration pathways (RCP 4.5 and RCP 8.5) climate scenarios in the 2050s and 2070s (average for 2041-2060, and 2061-2080), respectively. All models had satisfactory AUC values of more than 0.809, which are considered excellent. Jackknife tests revealed that Bio4 (temperature seasonality), land use, and population density were the main factors influencing RVF distribution in the region. From the risk maps generated, we infer that, without regulations, this disease might establish itself across more extensive areas in the region, including most of Rwanda and Burundi. The ongoing trade between East African countries and changing climates could intensify RVF spread into new geographic extents with suitable habitats for the important zoonosis. The predicted suitable areas for RVF in eastern Kenya, southern Tanzania, and Somalia overlaps to a large extent where cattle keeping and pastoralism are highly practiced, thereby signifying the urgency to manage and control the disease. This work validates RVF outbreak cases' effectiveness to map the disease's distribution, thus contributing to enhanced ecological modeling and improved disease tracking and control efforts in East Africa.