Prediction and preparedness against outbreaks with devastating economic impact

Rift Valley fever (RVF) outbreaks results in human disease and death and also large economic loss related to the consequences of livestock infection, abortions and perinatal mortality (>95%) in herds (sheep, goats, cattle, and camels) and export bans.

Early warning systems are needed to be able to elicit countermeasures in time. Existing approaches on modeling RVF outbreaks are based on flood prediction models and satellite-based information on rainfall, temperature and vegetation greenness.

However, in 2006 predictions of an outbreak were issued only after rains and flooding had already occurred. More information based on epidemiological and demographic data and a better understanding of environmental factors, ecology of RVF virus, and the socio-economic environment is required to fine-tune such models to develop practical early warning systems and appropriate countermeasures. 

We have sought to determine local, regional factors important for RVF outbreaks and use them to design a high resolution prediction model. We are characterising pastoralist spatio-temporal framework in relation to season and climate, RVF vector populations and densities, seroprevalence hence determining the factors that make specific geographic regions more prone to RVF outbreaks including looking in to cultural and equality factors (gender, community) that govern appearance vulnerability and consequences of RVF outbreaks with the goal of determining best practice for capacity-building for preparedness and prediction against RVF outbreaks.


  • Swedish International Development Cooperation Agency (Sida)


  • Umeå University
  • Ministry of Livestock Development, Kenya
  • Swedish University of Agricultural Sciences (SLU)