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dc.contributor.authorDunne, Michael
dc.contributor.authorMohammadi, Hossein
dc.contributor.authorChallenor, Peter
dc.contributor.authorBorgo, Rita
dc.contributor.authorPorphyre, Thibaud
dc.contributor.authorVernon, Ian
dc.contributor.authorFirat, Elif E.
dc.contributor.authorTurkay, Cagatay
dc.contributor.authorTorsney-Weir, Thomas
dc.contributor.authorGoldstein, Michael
dc.contributor.authorReeve, Richard
dc.contributor.authorFang, Hui
dc.contributor.authorSwallow, Ben
dc.date.accessioned2022-09-28T11:30:16Z
dc.date.available2022-09-28T11:30:16Z
dc.date.issued2022-06-01
dc.identifier281140372
dc.identifier9a9baf15-8420-40fe-bcd8-928135b17bea
dc.identifier000805050100001
dc.identifier85130609388
dc.identifier.citationDunne , M , Mohammadi , H , Challenor , P , Borgo , R , Porphyre , T , Vernon , I , Firat , E E , Turkay , C , Torsney-Weir , T , Goldstein , M , Reeve , R , Fang , H & Swallow , B 2022 , ' Complex model calibration through emulation, a worked example for a stochastic epidemic model ' , Epidemics , vol. 39 , 100574 . https://doi.org/10.1016/j.epidem.2022.100574en
dc.identifier.issn1755-4365
dc.identifier.otherORCID: /0000-0002-0227-2160/work/118411950
dc.identifier.otherPubMedCentral: PMC9109972
dc.identifier.urihttps://hdl.handle.net/10023/26084
dc.descriptionFunding: This work was supported by EPSRC, United Kingdom grant no. EP/R014604/1. RR was funded by STFC, United Kingdom grant no ST/V006126/1. IV gratefully acknowledges Wellcome funding (218261/Z/19/Z) and EPSRC funding (EP W011956). TP gratefully acknowledges funding from the Scottish Government Rural and Environment Science and Analytical Services Division, United Kingdom, as part of the Centre of Expertise on Animal Disease Outbreaks (EPIC). TP would also like to thank the French National Research Agency and Boehringer Ingelheim Animal Health France for support through the IDEXLYON project (ANR-16-IDEX-0005) and the Industrial Chair in Veterinary Public Health, as part of the VPH Hub in Lyon.en
dc.description.abstractUncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertain-ties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions.
dc.format.extent13
dc.format.extent2095129
dc.language.isoeng
dc.relation.ispartofEpidemicsen
dc.subjectUncertainty quantificationen
dc.subjectHistory matchingen
dc.subjectStochastic epidemic modelen
dc.subjectSEIRen
dc.subjectCalibrationen
dc.subjectCovid-19en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectT-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subjectMCCen
dc.subject.lccQA75en
dc.subject.lccRA0421en
dc.titleComplex model calibration through emulation, a worked example for a stochastic epidemic modelen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doi10.1016/j.epidem.2022.100574
dc.description.statusPeer revieweden


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