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dc.contributor.authorFreeman, Alex
dc.contributor.authorKerr, John
dc.contributor.authorRecchia, Gabriel
dc.contributor.authorSchneider, Claudia
dc.contributor.authorLawrence, Alice CE
dc.contributor.authorFinikarides, Leila
dc.contributor.authorLuoni, Giulia
dc.contributor.authorDryhurst, Sarah
dc.contributor.authorSpiegelhalter, David
dc.date.accessioned2021-12-11T00:31:21Z
dc.date.available2021-12-11T00:31:21Z
dc.date.issued2020-10-06
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331344
dc.description.abstractAs increasing amounts of data accumulate on the effects of the novel coronavirus Sars-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalised estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n=5,520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10,000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative ‘persona’ who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other ‘personas’ illustrating people who would face risks of a wide range of different levels. These ‘personas’ should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful.
dc.publisherCold Spring Harbor Laboratory
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleCommunicating personalised risks from COVID-19: guidelines from an empirical study
dc.typeArticle
dc.publisher.departmentDepartment of Pure Mathematics And Mathematical Statistics
dc.date.updated2021-12-10T09:14:16Z
prism.publicationDate2020
dc.identifier.doi10.17863/CAM.78792
rioxxterms.versionofrecord10.1101/2020.10.05.20206961
rioxxterms.versionVoR
dc.contributor.orcidFreeman, Alex [0000-0002-4115-161X]
dc.contributor.orcidKerr, John [0000-0002-6606-5507]
dc.contributor.orcidRecchia, Gabriel [0000-0002-0210-8635]
dc.contributor.orcidSchneider, Claudia [0000-0002-6612-5186]
dc.contributor.orcidDryhurst, Sarah [0000-0002-7772-8492]
dc.contributor.orcidSpiegelhalter, David [0000-0001-9350-6745]
rioxxterms.typeJournal Article/Review
pubs.funder-project-idDavid And Claudia Harding Foundation (unknown)
cam.depositDate2021-12-10
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International