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A dynamic microsimulation model for epidemics.

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cam.issuedOnline2021-10-18
dc.contributor.authorSpooner, Fiona
dc.contributor.authorAbrams, Jesse F
dc.contributor.authorMorrissey, Karyn
dc.contributor.authorShaddick, Gavin
dc.contributor.authorBatty, Michael
dc.contributor.authorMilton, Richard
dc.contributor.authorDennett, Adam
dc.contributor.authorLomax, Nik
dc.contributor.authorMalleson, Nick
dc.contributor.authorNelissen, Natalie
dc.contributor.authorColeman, Alex
dc.contributor.authorNur, Jamil
dc.contributor.authorJin, Ying
dc.contributor.authorGreig, Rory
dc.contributor.authorShenton, Charlie
dc.contributor.authorBirkin, Mark
dc.contributor.orcidMorrissey, Karyn [0000-0001-7259-1047]
dc.date.accessioned2022-01-06T12:56:14Z
dc.date.available2022-01-06T12:56:14Z
dc.date.issued2021-12
dc.date.updated2022-01-06T12:56:13Z
dc.descriptionFunder: Aerospace Technology Institute
dc.descriptionFunder: UK Research and Innovation
dc.descriptionFunder: The Alan Turing Institute
dc.description.abstractA large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.
dc.identifier.doi10.17863/CAM.79665
dc.identifier.eissn1873-5347
dc.identifier.issn0277-9536
dc.identifier.otherPMC8520832
dc.identifier.other34717286
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332219
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.publisher.urlhttp://dx.doi.org/10.1016/j.socscimed.2021.114461
dc.sourceessn: 1873-5347
dc.sourcenlmid: 8303205
dc.subjectCOVID-19
dc.subjectCoronavirus
dc.subjectDynamics
dc.subjectMicrosimulation
dc.subjectSEIR
dc.subjectSpatial-interaction
dc.subjectCOVID-19
dc.subjectCommunicable Disease Control
dc.subjectEpidemics
dc.subjectHumans
dc.subjectPolicy
dc.subjectSARS-CoV-2
dc.titleA dynamic microsimulation model for epidemics.
dc.typeArticle
dcterms.dateAccepted2021-10-05
prism.publicationNameSoc Sci Med
prism.volume291
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/T001569/1, EP/W006022/1)
pubs.funder-project-idEconomic and Social Research Council (ES/L011891/1)
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1016/j.socscimed.2021.114461

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