Recherche Impact pour un développement durable

Abstract:

Within the framework of their tripartite Alliance, FAO, WHO and OIE recognize their respective responsibilities to control diseases with a high health and economic impact, including zoonosis.

Repeated RVF epidemics in sub-Saharan Africa cause serious disease in small ruminant animals and humans. RVF disease has expanded its historical geographic range in the livestock-raising areas of eastern and southern Africa and into the Middle East (Saudi Arabia and Yemen) over the past 25 years, causing massive epidemics in Egypt, along the Mauritania–Senegal border and in Madagascar. A major outbreak in East Africa began in 2006 in northeastern Kenya, and spread into southern Somalia and Tanzania. Cattle, sheep, and humans are affected. Abortion storms with febrile disease and bloody diarrhea occur in ruminant animals, and mortality may be heavy in young stock. In sub-Saharan Africa, RVF virus is closely tied to its Aedes mosquito vectors. Field and laboratory workers need to work with caution to avoid becoming infected by exposure to the virus during postmortem examination of animals or processing materials in the laboratory. Both live, attenuated and inactivated vaccines are available for animals, but the unpredictability of scattered, sporadic RVF outbreaks across sub-Saharan Africa has been a major obstacle for triggering implementation of extensive, cost-effective vaccination programs.

The main goal of this research is to set up an artificial intelligence (AI) based early warning system for Rift Valley fever (RVF). An AI model based on a set of data collected on RVF, would allow an early detect outbreaks in order to prevent the spread of the virus (RVFV) in animal and human populations.