Rift Valley fever (RVF) is an emerging, vector-borne viral zoonosis that has significantly impacted public health, livestock health and production, and food security over the last three decades across large regions of the African continent and the Arabian Peninsula. The potential for expansion of RVF outbreaks within and beyond the range of past occurrence is unknown. Despite many large national and international epidemics in the region, the landscape epidemiology of RVF remains obscure, particularly with respect to the ecological roles of wildlife reservoirs and surface water features. This investigation delineated RVF risk throughout Africa and the Arabian Peninsula as a function of a suite of biotic and abiotic landscape features using a machine learning classification system. The distribution of all human and animal RVF outbreaks from 1998 to 2016 was modeled using climate, surface water, land cover, livestock and wild mammal densities, human population density, human migration, and livestock production systems. Predicted risk, as derived from this model, was subsequently mapped.
Proximity to swamp and freshwater marsh, livestock density, species richness density of wild ruminants and rodents, and the ecologic niche of Aedes mosquitoes were associated with increased landscape suitability to RVF outbreaks. In addition, two tiers of analyses suggested that wild ruminants and rodents may participate in the infection ecology of RVF. These results highlight the importance of vigilant RVF biosurveillance in national and international livestock industries within the African continent and Arabian Peninsula. In addition, the structure of surface water in the landscape and the role of wildlife hosts in RVF virus circulation and subsequent outbreaks may be underappreciated. These results await validation by studies employing a deeper, field-based interrogation of potential wildlife hosts within high risk taxa and in proximity to diverse surface water regimes.