Modelling the persistence and control of Rift Valley fever virus in a spatially heterogeneous landscape

Warren S. D. Tennant (), Eric Cardinale, Catherine Cêtre-Sossah, Youssouf Moutroifi, Gilles Le Godais, Davide Colombi, Simon E. F. Spencer, Mike J. Tildesley, Matt J. Keeling, Onzade Charafouddine, Vittoria Colizza, W. John Edmunds and Raphaëlle Métras
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Warren S. D. Tennant: University of Warwick
Eric Cardinale: UMR Animal, Santé, Territoires, Risques, et Écosystèmes
Catherine Cêtre-Sossah: UMR Animal, Santé, Territoires, Risques, et Écosystèmes
Youssouf Moutroifi: l’Elevage, la Pêche, l’Industrie, l’Energie et l’Artisanat
Gilles Le Godais: de l’Agriculture et de la Forêt de Mayotte
Davide Colombi: Aizoon Technology Consulting
Simon E. F. Spencer: University of Warwick
Mike J. Tildesley: University of Warwick
Matt J. Keeling: University of Warwick
Onzade Charafouddine: l’Elevage, la Pêche, l’Industrie, l’Energie et l’Artisanat
Vittoria Colizza: INSERM, Sorbonne Université, Institut Pierre Louis d’Épidémiologie et de Santé Publique (Unité Mixte de Recherche en Santé 1136)
W. John Edmunds: London School of Hygiene and Tropical Medicine
Raphaëlle Métras: INSERM, Sorbonne Université, Institut Pierre Louis d’Épidémiologie et de Santé Publique (Unité Mixte de Recherche en Santé 1136)

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract The persistence mechanisms of Rift Valley fever (RVF), a zoonotic arboviral haemorrhagic fever, at both local and broader geographical scales have yet to be fully understood and rigorously quantified. We developed a mathematical metapopulation model describing RVF virus transmission in livestock across the four islands of the Comoros archipelago, accounting for island-specific environments and inter-island animal movements. By fitting our model in a Bayesian framework to 2004–2015 surveillance data, we estimated the importance of environmental drivers and animal movements on disease persistence, and tested the impact of different control scenarios on reducing disease burden throughout the archipelago. Here we report that (i) the archipelago network was able to sustain viral transmission in the absence of explicit disease introduction events after early 2007, (ii) repeated outbreaks during 2004–2020 may have gone under-detected by local surveillance, and (iii) co-ordinated within-island control measures are more effective than between-island animal movement restrictions.

Date: 2021
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DOI: 10.1038/s41467-021-25833-8

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