An open source framework for Rift Valley Fever forecasting

Rift Valley Fever (RVF) is a complex disease with devastating public health and economic costs. It is transmitted directly from livestock to people and is maintained via mosquito transmission in livestock populations. As a zoonotic disease with a vector component, outbreaks of RVF are tightly linked to climatic and environmental conditions.

Statistical modeling approaches have been developed to forecast RVF in Africa, but performance has been inconsistent across regions, with higher predictive accuracy in East Africa than in Southern Africa. Current continent-wide models, which use the Normalized Difference Vegetation Index (NDVI) to predict RVF activity, do not capture local attributes, such as livestock density and land cover, which may explain differing performance.

We have assembled a team that includes local South Africa (RSA) government stakeholders, and co-created a tool for RSA that builds on previous work to create an RVF Early Warning System for RSA. Our goal is to package this new, open, locally customisable tool developed for RSA for deployment to other impacted regions. The intent is that end-users (farmers, farmer associations and others) would be forewarned when to vaccinate their livestock against RVF, a month to three months in advance of potential RVF activity.