A combination of probabilistic and mechanistic approaches for predicting the spread of African swine fever on Merry Island

Munoz Facundo, Pleydell David, Jori Ferran. 2022. A combination of probabilistic and mechanistic approaches for predicting the spread of African swine fever on Merry Island. Epidemics, 40:100596, 14 p.

Résumé : Over the last decade African swine fever virus, one of the most virulent pathogens known to affect pigs, has devastated pork industries and wild pig populations throughout the world. Despite a growing literature on specific aspects of African swine fever transmission dynamics, it remains unclear which methods and approaches are most effective for controlling the disease during a crisis. As a consequence, an international modelling challenge was organized in which teams analyzed and responded to a stream of data from an in silico outbreak in the fictive country of Merry Island. In response to this outbreak, we developed a modelling approach that aimed to predict the evolution of the epidemic and evaluate the impact of potential control measures. Two independent models were developed: a stochastic mechanistic space–time compartmental model for characterizing the dissemination of the virus among wild boar; and a deterministic probabilistic risk model for quantifying infection probabilities in domestic pig herds. The combined results of these two models provided valuable information for anticipating the main risks of dissemination and maintenance of the virus (speed and direction of African swine fever spread among wild boar populations, pig herds at greatest risk of infection, the size of the epidemic in the short and long terms), for evaluating the impact of different control measures and for providing specific recommendations concerning control interventions.

Mots-clés Agrovoc : peste porcine africaine, épidémiologie, contrôle de maladies, modélisation, théorie Bayésienne, échantillonnage

Mots-clés complémentaires : Expérimentation in silico

Mots-clés libres : African swine fever, Epidemiology, Bayesian modelling, MCMC, Synthetic likelihood

Classification Agris : L73 - Maladies des animaux
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Auteurs et affiliations

• Munoz Facundo, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-5061-4241 - auteur correspondant
• Pleydell David, INRAE (FRA) - auteur correspondant
• Jori Ferran, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0001-5451-7767 - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/601410/)

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