Ability of a dynamical climate sensitive disease model to reproduce historical Rift Valley Fever outbreaks over Africa - (:unkn) unknown (My University, 2024)
Rift Valley Fever (RVF) is a zoonosis transmitted by Aedes and Culex mosquitoes, and is considered a priority pathogen by the WHO. RVF epidemics mostly occur in Africa and can decimate livestock herds, causing significant economic losses and posing health risks for humans. RVF transmission is associated with the occurrence of El Niño events that cause floods in eastern Africa and favour the emergence of mosquitoes in wetlands. Different risk models have been developed to forecast RVF transmission risk but very few studies have validated models at pan-African scale. This study aims to validate the skill of the Liverpool Rift Valley Fever model (LRVF) in reproducing RVF epidemics over Africa and to explore the relationship between simulated climatic suitability for RVF transmission and large-scale climate modes of variability such as the El Niño Southern Oscillation (ENSO) and the Dipole Mode Index (DMI). Our results show that the LRVF model correctly simulates RVF transmission hotspots and reproduces large epidemics that affected African countries. LRVF was able to correctly reproduce major RVF epidemics in Somalia, Kenya, Zambia and to a lesser extent for Mauritania and Senegal. The positive phases of ENSO and DMI are associated with an increased risk of RVF over the Horn of Africa, with important time lags. Following research activities should focus on the development of predictive modelling systems at different time scales. Rift Valley fever (RVF) is a zoonotic mosquito-borne disease that was first isolated and discovered in Kenya in 1931 1. Rift Valley fever virus (RVFV) belongs to the genus Phlebovirus in the family Bunyaviridae 2. Rift Valley Fever is a vector-borne disease that primarily affects goats and sheep but can also infect cattle, camels, other wildlife animals (antelopes and wildebeest) and humans 3. Epizootics of RVF are characterized by abortion storms, increased mortality rates in livestock and significant economic losses. Infection
Keywords: African Research