The African swine fever modelling challenge: Model comparison and lessons learnt
2022
Ezanno, Pauline | Picault, Sébastien | Bareille, Servane | Beaunée, Gaël | Boender, Gert Jan | Dankwa, Emmanuelle, A. | Deslandes, François | Donnelly, Christl, A. | Hagenaars, Thomas, J. | Hayes, Sarah | Jori, Ferran | Lambert, Sébastien | Mancini, Matthieu | Munoz, Facundo | Pleydell, David R.J. | Thompson, Robin, N. | Vergu, Elisabeta | Vignes, Matthieu | Vergne, Timothée | Biologie, Epidémiologie et analyse de risque en Santé Animale (BIOEPAR) ; École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Interactions hôtes-agents pathogènes [Toulouse] (IHAP) ; Ecole Nationale Vétérinaire de Toulouse (ENVT) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Wageningen BioVeterinary Research ; Wageningen University and Research [Wageningen] (WUR) | Department of Statistics [Oxford] ; University of Oxford | Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Department of Infectious Disease Epidemiology [London] (DIDE) ; Imperial College London | Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Department of Pathobiology and Population Sciences ; Royal Veterinary College | CEEED Centre for Emerging, Endemic and Exotic Diseases ; Royal Veterinary College | Warwick Mathematics Institute (WMI) ; University of Warwick [Coventry] | University of Warwick [Coventry] | Massey University | This work was supported by the animal health division of National Research Institute for Agriculture, Food and the Environment (INRAE) (France) [PPApred project]. For intensive computing resources, we used the Bioinformatics Core Facility of Nantes BiRD, member of Biogenouest, Institut Français de Bioinformatique (IFB) (ANR-11-INBS-0013), and the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility, http://doi.org/10.15454/1.5572390655343293E12). | ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011)
International audience
اظهر المزيد [+] اقل [-]إنجليزي. Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health – the ASF Challenge – which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique