Individual-based network model for Rift Valley fever in Kabale District, Uganda.

Abstract

Rift Valley fever (RVF) is a zoonotic disease, that causes significant morbidity and mortality among ungulate livestock and humans in endemic regions. In East Africa, the causative agent of the disease is Rift Valley fever virus (RVFV) which is primarily transmitted by multiple mosquito species in Aedes and Mansonia genera during both epizootic and enzootic periods in a complex transmission cycle largely driven by environmental and climatic factors. However, recent RVFV activity in Uganda demonstrated the capability of the virus to spread into new regions through livestock movements, and underscored the need to develop effective mitigation strategies to reduce transmission and prevent spread among cattle populations. We simulated RVFV transmission among cows in 22 different locations of the Kabale District in Uganda using real world livestock data in a network-based model. This model considered livestock as a spatially explicit factor in different locations subjected to specific vector and environmental factors, and was configured to investigate and quantitatively evaluate the relative impacts of mosquito control, livestock movement, and diversity in cattle populations on the spread of the RVF epizootic. We concluded that cattle movement should be restricted for periods of high mosquito abundance to control epizootic spreading among locations during an RVF outbreak. Importantly, simulation results also showed that cattle populations with heterogeneous genetic diversity as crossbreeds were less susceptible to infection compared to homogenous cattle populations.