Thesis Cécilia Hélène

Cecilia Hélène

Modeling Rift Valley fever transmission dynamics : insight from micro- to macro-scale studies

Abstract :

Rift Valley fever (RVF-V) is a viral, vector-borne, zoonotic disease. It affects multiple hosts, mainly livestock such as cattle, sheep, and goats. We aimed at enhancing knowledge on the relative contribution of host species to transmission, focusing on Senegal, a region with recurring RVFV circulation. With a systematic review of existing mechanistic models of RVFV transmission dynamics, we identified critical knowledge gaps and characterised the diversity of modelling approaches. We mapped RVF epidemic potential in northern Senegal for three consecutive rainy seasons (2014-2016), using the basic reproduction number. September was identified as a period of high risk of amplification following RVFV introduction. To quantify differences in infectiousness between host species, we developed the first within-host model of RVFV production by host cells, and
estimated the relationship between host infectious viral loads and the probability to infect mosquitoes. The results showed that sheep were the most infectious host species, and that Aedes vectors were more likely to get infected than Culex when fed with similarly infectious bloodmeals. We incorporated this heterogeneity into a metapopulation model representing seasonal herd movements in northern Senegal. We quantified the delay between RVFV introduction through nomadic herds and infection of sedentary populations. We highlighted a systematic earlier infection in cattle than goats and sheep. This PhD used modeling to disentangle the complex transmission dynamics of a multi-host multi-vector pathogen, highlighting possible avenues for the development of efficient and targeted control strategies, crucial to ultimately prevent human cases.

Key words :

Rift Valley fever virus, mathematical modeling, vector-borne disease, multi-host, Senegal

Modification date : 11 September 2023 | Publication date : 07 February 2022 | Redactor : ML