Modelling Vaccination Strategies against Rift Valley Fever in Livestock in Kenya

Fournié

Dommergues³

et al. 2017

PLoS Negl Trop Dis

Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006–2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data.

Section: Methodsmentioning

confidence: 99%

Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach

Fournié

Dommergues³

et al. 2017

PLoS Negl Trop Dis

“…The impact and cost-effectiveness of livestock vaccination have been assessed in specific RVF high-risk areas in Kenya using simulation modeling (40,41). Our analysis demonstrates the impact of vaccination strategies on reducing the number of human and animal cases, through a model calibrated from epidemic data.…”

Section: Discussionmentioning

confidence: 97%

Estimation of Rift Valley fever virus spillover to humans during the Mayotte 2018–2019 epidemic

Proc. Natl. Acad. Sci. U.S.A.

Edmunds

Youssouffi³

et al. 2020

Rift Valley fever (RVF) is an emerging, zoonotic, arboviral hemorrhagic fever threatening livestock and humans mainly in Africa. RVF is of global concern, having expanded its geographical range over the last decades. The impact of control measures on epidemic dynamics using empirical data has not been assessed. Here, we fitted a mathematical model to seroprevalence livestock and human RVF case data from the 2018–2019 epidemic in Mayotte to estimate viral transmission among livestock, and spillover from livestock to humans through both direct contact and vector-mediated routes. Model simulations were used to assess the impact of vaccination on reducing the epidemic size. The rate of spillover by direct contact was about twice as high as vector transmission. Assuming 30% of the population were farmers, each transmission route contributed to 45% and 55% of the number of human infections, respectively. Reactive vaccination immunizing 20% of the livestock population reduced the number of human cases by 30%. Vaccinating 1 mo later required using 50% more vaccine doses for a similar reduction. Vaccinating only farmers required 10 times as more vaccine doses for a similar reduction in human cases. Finally, with 52.0% (95% credible interval [CrI] [42.9–59.4]) of livestock immune at the end of the epidemic wave, viral reemergence in the next rainy season (2019–2020) is unlikely. Coordinated human and animal health surveillance, and timely livestock vaccination appear to be key to controlling RVF in this setting. We furthermore demonstrate the value of a One Health quantitative approach to surveillance and control of zoonotic infectious diseases.

“…Finally, the livestock model was built with similar assumptions than in our previous paper (9). This included a latent (E) and an infectious (I) period of 7 days in livestock, accounting for the extrinsic incubation period in the vector (3-7 days), and the latent (1-6 days) and infectious stages (3-6 days) in livestock (30)(31)(32)(33), without explicitly modelling these processes. Although this may have slightly impacted on the predicted timing of the epidemic peak in humans, our model predictions were in agreement with the observations.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Rift Valley fever virus spillover to humans during the Mayotte 2018-2019 epidemic

Edmunds²,

Youssouffi³

et al. 2020

Preprint

Rift Valley fever (RVF) is an emerging, zoonotic, arboviral haemorrhagic fever threatening livestock and humans mainly in Africa. RVF is of global concern, having expanded its geographical range over the last decades. The impact of control measures on epidemic dynamics using empirical data has not been assessed. Here, we combined seroprevalence livestock and human RVF case data from the 2018-2019 epidemic in Mayotte, with a dynamic mathematical model. Using a Bayesian inference framework, we estimated viral transmission potential amongst livestock, and spillover from livestock to humans, through both direct contact and vector-mediated routes. Model simulations were used to assess the impact of vaccination on reducing the human epidemic size. Reactive vaccination immunising 20% of the livestock population reduced the number of human cases by 30%. To achieve a similar impact, delaying the vaccination by one month required using 50% more vaccine doses, and vaccinating only humans required 20 times as more as the number of doses for livestock. Finally, with 53. ) of livestock estimated to be immune at the end of the epidemic wave, viral re-emergence in the next rainy season (2019-2020) was unlikely. We present the first mathematical model for RVF fitted to real-world data to estimate virus transmission parameters, and able to inform potential control programmes. Human and animal health surveillance, and timely livestock vaccination appear to be key in reducing disease risk in humans. We furthermore demonstrate the value of a One Health quantitative approach to surveillance and control of zoonotic infectious diseases.