The African swine fever modelling challenge: Model comparison and lessons learnt

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dc.citation.volume40
dc.contributor.authorEzanno P
dc.contributor.authorPicault S
dc.contributor.authorBareille S
dc.contributor.authorBeaunée G
dc.contributor.authorBoender GJ
dc.contributor.authorDankwa EA
dc.contributor.authorDeslandes F
dc.contributor.authorDonnelly CA
dc.contributor.authorHagenaars TJ
dc.contributor.authorHayes S
dc.contributor.authorJori F
dc.contributor.authorLambert S
dc.contributor.authorMancini M
dc.contributor.authorMunoz F
dc.contributor.authorPleydell DRJ
dc.contributor.authorThompson RN
dc.contributor.authorVergu E
dc.contributor.authorVignes M
dc.contributor.authorVergne T
dc.coverage.spatialNetherlands
dc.date.accessioned2024-01-25T20:38:18Z
dc.date.accessioned2024-07-25T06:43:34Z
dc.date.available2022-07-27
dc.date.available2024-01-25T20:38:18Z
dc.date.available2024-07-25T06:43:34Z
dc.date.issued2022-09
dc.description.abstractRobust 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.
dc.description.confidentialfalse
dc.edition.editionSeptember 2022
dc.format.pagination100615-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/35970067
dc.identifier.citationEzanno P, Picault S, Bareille S, Beaunée G, Boender GJ, Dankwa EA, Deslandes F, Donnelly CA, Hagenaars TJ, Hayes S, Jori F, Lambert S, Mancini M, Munoz F, Pleydell DRJ, Thompson RN, Vergu E, Vignes M, Vergne T. (2022). The African swine fever modelling challenge: Model comparison and lessons learnt.. Epidemics. 40. (pp. 100615-).
dc.identifier.doi10.1016/j.epidem.2022.100615
dc.identifier.eissn1878-0067
dc.identifier.elements-typejournal-article
dc.identifier.issn1755-4365
dc.identifier.number100615
dc.identifier.piiS1755-4365(22)00057-3
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70751
dc.languageeng
dc.publisherElsevier BV
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1755436522000573
dc.relation.isPartOfEpidemics
dc.rights(c) 2022 The Author/s
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectControl measures
dc.subjectEnsemble model
dc.subjectForecast
dc.subjectSpatio-temporal epidemiological model
dc.subjectSwine
dc.subjectWild boar
dc.subjectAfrican Swine Fever
dc.subjectAfrican Swine Fever Virus
dc.subjectAnimals
dc.subjectAnimals, Wild
dc.subjectEpidemics
dc.subjectSus scrofa
dc.subjectSwine
dc.titleThe African swine fever modelling challenge: Model comparison and lessons learnt
dc.typeJournal article
pubs.elements-id455467
pubs.organisational-groupOther
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