Rift Valley fever in Kenyan pastoral livestock: Simulation with an individual-based demographic model
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Fuhrimann, S., Kimani, T., Hansen, F., Bett, B., Zinsstag, J. and Schelling, E. 2012. Rift Valley fever in Kenyan pastoral livestock: Simulation with an individual-based demographic model. Poster prepared for the 13th conference of the International Society for Veterinary Epidemiology and Economics, Maastricht, the Netherlands, 20-24 August 2012.
Permanent link to this item: http://hdl.handle.net/10568/24876
Rift Valley Fever (RVF) is a viral zoonosis and a mosquito-borne disease caused by a phlebovirus in the family Bunyaviridae. It affects livestock, humans and wildlife. Epidemic outbreaks of RVF in East Africa, which occur after heavy rainfalls in cycles of 5-15 years, have caused next to human morbidity and mortality considerable economic losses throughout the livestock production and market chain. Establishment of a pastoral livestock demographic model simulating alternating normal and drought periods (appropriate for the Sahel) and RVF epidemics. We have developed an individual-based model to simulate the following scenarios (1) the demographic dynamics of cattle, camels, sheep and goats in North Eastern-Province; (2) an RVF outbreak in livestock and the RVF immunity status afterwards; and (3) impacts of control measures (combinations of vaccination, sanitary measures, surveillance, vector control and awareness campaigns). The baseline and RVF-attributable mortalities can be simulated and show the losses due to RVF. Further, we can retrieve proportions of affected animals, grouped in species, age classes and sex, as well as the number of infected slaughtered and sold animals. Sheep and goats are most likely to spread the disease through livestock trade. Slaughtered infected sheep are an important risk factor to human RVF infection. Our results assist in the assessment of cost-benefit and cost-effectiveness of interventions which should improve future intersectoral livestock – public health contingency planning. The ratio of susceptible/immune hosts can further support the prediction system by consideration of the immunity levels years after a previous outbreak.