A Stochastic Model to Study Rift Valley Fever Persistence with Different Seasonal Patterns of Vector Abundance: New Insights on the Endemicity in the Tropical Island of Mayotte
Rift Valley fever (RVF) is a zoonotic vector-borne disease causing abortion storms in cattle and human epidemics in Africa. Our aim was to evaluate RVF persistence in a seasonal and isolated population and to apply it to Mayotte Island (Indian Ocean), where the virus was still silently circulating four years after its last known introduction in 2007. We proposed a stochastic model to estimate RVF persistence over several years and under four seasonal patterns of vector abundance. Firstly, the model predicted a wide range of virus spread patterns, from obligate persistence in a constant or tropical environment (without needing vertical transmission or reintroduction) to frequent extinctions in a drier climate. We then identified for each scenario of seasonality the parameters that most influenced prediction variations. Persistence was sensitive to vector lifespan and biting rate in a tropical climate, and to host viraemia duration and vector lifespan in a drier climate. The first epizootic peak was primarily sensitive to viraemia duration and thus likely to be controlled by vaccination, whereas subsequent peaks were sensitive to vector lifespan and biting rate in a tropical climate, and to host birth rate and viraemia duration in arid climates. Finally, we parameterized the model according to Mayotte known environment. Mosquito captures estimated the abundance of eight potential RVF vectors. Review of RVF competence studies on these species allowed adjusting transmission probabilities per bite. Ruminant serological data since 2004 and three new cross-sectional seroprevalence studies are presented. Transmission rates had to be divided by more than five to best fit observed data. Five years after introduction, RVF persisted in more than 10% of the simulations, even under this scenario of low transmission. Hence, active surveillance must be maintained to better understand the risk related to RVF persistence and to prevent new introductions.
CRVOI, Centre de Recherche et de Veille sur les maladies émergentes dans l’Océan Indien, F‐97490 Sainte Clotilde, La Réunion, France;CIRAD, UMR CMAEE, F‐97490, Sainte Clotilde, France;INRA, UMR 1309 CMAEE, F‐34398, Montpellier, France;AgroParisTech, F-75005, Paris, France;Université de la Réunion, F‐97715 Saint Denis, La Réunion, France;INRA, Oniris, LUNAM Université, UMR1300 BioEpAR, CS 40706, F‐44307, Nantes, France;INRA, Oniris, LUNAM Université, UMR1300 BioEpAR, CS 40706, F‐44307, Nantes, France;CRVOI, Centre de Recherche et de Veille sur les maladies émergentes dans l’Océan Indien, F‐97490 Sainte Clotilde, La Réunion, France;CIRAD, UMR CMAEE, F‐97490, Sainte Clotilde, France;INRA, UMR 1309 CMAEE, F‐34398, Montpellier, France;GDS Mayotte—Coopérative Agricole des Eleveurs Mahorais, F-97670 Coconi, Mayotte, France;ARS OI, F-97600 Mamoudzou, Mayotte, France;CRVOI, Centre de Recherche et de Veille sur les maladies émergentes dans l’Océan Indien, F‐97490 Sainte Clotilde, La Réunion, France;CIRAD, UMR CMAEE, F‐97490, Sainte Clotilde, France;INRA, UMR 1309 CMAEE, F‐34398, Montpellier, France
Recommended Citation:
Lisa Cavalerie,Maud V. P. Charron,Pauline Ezanno,et al. A Stochastic Model to Study Rift Valley Fever Persistence with Different Seasonal Patterns of Vector Abundance: New Insights on the Endemicity in the Tropical Island of Mayotte[J]. PLOS ONE,2015-01-01,10(7)