Abstract
Rift Valley fever (RVF) is a vector-borne disease that has severe impacts on livelihoods, national and international markets, and human health. RVF is currently limited to Africa and parts of the Near East with the potential to expand globally. In livestock, the disease affects sheep, goats, cattle, buffaloes, and camels. Outbreaks are closely associated with climate anomalies (e.g., periods of heavy rains and prolonged flooding), which increase habitat suitability for vector populations, influencing the risk of disease emergence, transmission and spread. Early warning systems represent an essential tool to enable national authorities to implement measures preventing outbreaks. In this context, the Food and Agriculture Organization of the United Nations (FAO) has developed a web-based RVF Early Warning Decision Support Tool (RVF DST), which integrates near real-time RVF risk maps with geospatial data, RVF historical and current disease events from EMPRES Global Animal Disease Information System (EMPRES-i) and expert knowledge on eco-epidemiology. This tool has been crucial in successfully forecasting hotspots for RVF vector amplification, as it provides recommendations and early warning messages for countries at risk of RVF outbreaks. The tool is used to build capacity for early warning and forecasting at country level, and demonstrates how near real-time modelling, risk forecasting and digital innovation can enhance preparedness and anticipatory actions.