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dc.contributor.authorCresswell, R
dc.contributor.authorAugustin, D
dc.contributor.authorBouros, I
dc.contributor.authorFarm, HJ
dc.contributor.authorMiao, S
dc.contributor.authorAhern, A
dc.contributor.authorRobinson, M
dc.contributor.authorLemenuel-Diot, A
dc.contributor.authorGavaghan, DJ
dc.contributor.authorLambert, B
dc.contributor.authorThompson, RN
dc.date.accessioned2022-05-05T13:44:35Z
dc.date.issued2022-08-15
dc.date.updated2022-05-05T13:30:31Z
dc.description.abstractDuring infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (𝑅t), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of 𝑅t varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide 𝑅t, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on 𝑅t estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate 𝑅t, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect 𝑅t estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate 𝑅t.en_GB
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)en_GB
dc.description.sponsorshipClarendon Funden_GB
dc.description.sponsorshipUKRIen_GB
dc.description.sponsorshipRoche Pharmaceutical Researchen_GB
dc.identifier.citationVol. 380 (2233), article 20210308en_GB
dc.identifier.doi10.1098/rsta.2021.0308
dc.identifier.grantnumberEP/S024093/1en_GB
dc.identifier.grantnumberEP/V053507/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/129526
dc.language.isoenen_GB
dc.publisherRoyal Societyen_GB
dc.relation.urlhttps://sabs-r3-epidemiology.github.io/branchproen_GB
dc.relation.urlhttps://github.com/SABS-R3-Epidemiology/transmission-heterogeneity-resultsen_GB
dc.relation.urlhttps://github.com/SABS-R3-Epidemiology/branchproen_GB
dc.rights© 2022 The Authors. Open access. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.en_GB
dc.subjectmathematical modellingen_GB
dc.subjectreproduction numberen_GB
dc.subjectimported casesen_GB
dc.subjectbranching processesen_GB
dc.subjectCOVID-19en_GB
dc.subjectSARS-CoV-2en_GB
dc.titleHeterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction numberen_GB
dc.typeArticleen_GB
dc.date.available2022-05-05T13:44:35Z
dc.identifier.issn1471-2962
dc.descriptionThis is the final version. Available on open access from the Royal Society via the DOI in this recorden_GB
dc.descriptionData Accessibility: The user-friendly web interface for estimating Rt while accounting for different transmission risks from local and imported cases can be found at https://sabs-r3-epidemiology.github.io/branchpro. All data and computing scripts required to reproduce the results presented here are available at https://github.com/SABS-R3-Epidemiology/transmission-heterogeneity-results. The source code of the branchpro Python package, which we developed to perform the inference presented in this article, is available at https://github.com/SABS-R3-Epidemiology/branchpro. No restrictions exist on data availability.en_GB
dc.identifier.journalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciencesen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/  en_GB
dcterms.dateAccepted2022-05-04
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-05-04
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2022-05-05T13:30:38Z
refterms.versionFCDAM
refterms.dateFOA2022-08-24T14:25:37Z
refterms.panelBen_GB


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© 2022 The Authors. Open access. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Except where otherwise noted, this item's licence is described as © 2022 The Authors. Open access. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.