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Stochastic modelling of African swine fever in wild boar and domestic pigs : epidemic forecasting and comparison of disease management strategies
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Dankwa, Emmanuelle A., Lambert, Sébastien, Hayes, Sarah, Thompson, Robin N. and Donnelly, Christl A. (2022) Stochastic modelling of African swine fever in wild boar and domestic pigs : epidemic forecasting and comparison of disease management strategies. Epidemics, 40 . 100622. doi:10.1016/j.epidem.2022.100622 ISSN 1755-4365.
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Official URL: https://doi.org/10.1016/j.epidem.2022.100622
Abstract
African swine fever (ASF), caused by the African swine fever virus (ASFV), is highly virulent in domestic pigs and wild boar (Sus scrofa), causing up to 100% mortality. The recent epidemic of ASF in Europe has had a serious economic impact and poses a threat to global food security. Unfortunately, there is no effective treatment or vaccine against ASFV, limiting the available disease management strategies. Mathematical models allow us to further our understanding of infectious disease dynamics and evaluate the efficacy of disease management strategies. The ASF Challenge, organised by the French National Research Institute for Agriculture, Food, and the Environment, aimed to expand the development of ASF transmission models to inform policy makers in a timely manner. Here, we present the model and associated projections produced by our team during the challenge. We developed a stochastic model combining transmission between wild boar and domestic pigs, which was calibrated to synthetic data corresponding to different phases describing the epidemic progression. The model was then used to produce forward projections describing the likely temporal evolution of the epidemic under various disease management scenarios. Despite the interventions implemented, long-term projections forecasted persistence of ASFV in wild boar, and hence repeated outbreaks in domestic pigs. A key finding was that it is important to consider the timescale over which different measures are evaluated: interventions that have only limited effectiveness in the short term may yield substantial long-term benefits. Our model has several limitations, partly because it was developed in real-time. Nonetheless, it can inform understanding of the likely development of ASF epidemics and the efficacy of disease management strategies, should the virus continue its spread in Europe.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QR Microbiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Mathematical models, Stochastic models, African swine fever virus | ||||||||
Journal or Publication Title: | Epidemics | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 1755-4365 | ||||||||
Official Date: | September 2022 | ||||||||
Dates: |
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Volume: | 40 | ||||||||
Article Number: | 100622 | ||||||||
DOI: | 10.1016/j.epidem.2022.100622 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 17 August 2022 | ||||||||
Date of first compliant Open Access: | 17 August 2022 | ||||||||
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