{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T01:38:41Z","timestamp":1789609121579,"version":"build-2803163510"},"reference-count":96,"publisher":"The Royal Society","issue":"6","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":1458,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":1458,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":1458,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/royalsociety.org\/journals\/ethics-policies\/data-sharing-mining\/"}],"funder":[{"name":"Medical Research Council","award":["MR\/R015600\/1"],"award-info":[{"award-number":["MR\/R015600\/1"]}]},{"DOI":"10.13039\/501100000272","name":"National Institute of Health Research","doi-asserted-by":"crossref","award":["NIHR200907"],"award-info":[{"award-number":["NIHR200907"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["royalsocietypublishing.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The coronavirus disease 2019 (COVID-19) pandemic has disrupted the lives of billions across the world. Mathematical modelling has been a key tool deployed throughout the pandemic to explore the potential public health impact of an unmitigated epidemic. The results of such studies have informed governments' decisions to implement non-pharmaceutical interventions to control the spread of the virus. In this article, we explore the complex relationships between models, decision-making, the media and the public during the COVID-19 pandemic in the United Kingdom of Great Britain and Northern Ireland (UK). Doing so not only provides an important historical context of COVID-19 modelling and how it has shaped the UK response, but as the pandemic continues and looking towards future pandemic preparedness, understanding these relationships and how they might be improved is critical. As such, we have synthesized information gathered via three methods: a survey to publicly list attendees of the Scientific Advisory Group for Emergencies, the Scientific Pandemic Influenza Group on Modelling and other comparable advisory bodies, interviews with science communication experts and former scientific advisors, and reviewing some of the key COVID-19 modelling literature from 2020. Our research highlights the desire for increased bidirectional communication between modellers, decision-makers and the public, as well as the need to convey uncertainty inherent in transmission models in a clear manner. These aspects should be considered carefully ahead of the next emergency response.<\/jats:p>","DOI":"10.1098\/rsfs.2021.0013","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T13:23:51Z","timestamp":1633958631000},"update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Disease transmission and control modelling at the science\u2013policy interface"],"prefix":"10.1098","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-9103","authenticated-orcid":true,"given":"Ruth","family":"McCabe","sequence":"first","affiliation":[{"name":"24\u201329 St Giles', OX1 3LB, Oxford, UK 1","department":["Department of Statistics, University of Oxford,"]},{"name":"UK 2","department":["NIHR Health Protection Research Unit in Emerging and Zoonotic Infections,"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0195-2463","authenticated-orcid":true,"given":"Christl A.","family":"Donnelly","sequence":"additional","affiliation":[{"name":"24\u201329 St Giles', OX1 3LB, Oxford, UK 1","department":["Department of Statistics, University of Oxford,"]},{"name":"UK 2","department":["NIHR Health Protection Research Unit in Emerging and Zoonotic Infections,"]},{"name":"London, UK 3","department":["MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London,"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"175","reference":[{"key":"2026010712440071100_RSFS20210013C1","author":"World Health Organization. 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