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dc.contributor.authorEales, Oliver
dc.contributor.authorAinslie, Kylie E C
dc.contributor.authorWalters, Caroline E
dc.contributor.authorWang, Haowei
dc.contributor.authorAtchison, Christina
dc.contributor.authorAshby, Deborah
dc.contributor.authorDonnelly, Christl A
dc.contributor.authorCooke, Graham
dc.contributor.authorBarclay, Wendy
dc.contributor.authorWard, Helen
dc.contributor.authorDarzi, Ara
dc.contributor.authorElliott, Paul
dc.contributor.authorRiley, Steven
dc.date.accessioned2022-07-31T19:44:06Z
dc.date.available2022-07-31T19:44:06Z
dc.date.issued2022-06-22
dc.identifier.pmid35780515
dc.identifier.doi10.1016/j.epidem.2022.100604
dc.identifier.urihttp://hdl.handle.net/10029/625947
dc.description.abstractThe time-varying reproduction number (Rt) can change rapidly over the course of a pandemic due to changing restrictions, behaviours, and levels of population immunity. Many methods exist that allow the estimation of Rt from case data. However, these are not easily adapted to point prevalence data nor can they infer Rt across periods of missing data. We developed a Bayesian P-spline model suitable for fitting to a wide range of epidemic time-series, including point-prevalence data. We demonstrate the utility of the model by fitting to periodic daily SARS-CoV-2 swab-positivity data in England from the first 7 rounds (May 2020-December 2020) of the REal-time Assessment of Community Transmission-1 (REACT-1) study. Estimates of Rt over the period of two subsequent rounds (6-8 weeks) and single rounds (2-3 weeks) inferred using the Bayesian P-spline model were broadly consistent with estimates from a simple exponential model, with overlapping credible intervals. However, there were sometimes substantial differences in point estimates. The Bayesian P-spline model was further able to infer changes in Rt over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in Rt over the summer of 2020 as restrictions were eased, and a reduction in Rt during England's second national lockdown followed by an increase as the Alpha variant surged. The model is robust against both under-fitting and over-fitting and is able to interpolate between periods of available data; it is a particularly versatile model when growth rate can change over small timescales, as in the current SARS-CoV-2 pandemic. This work highlights the importance of pairing robust methods with representative samples to track pandemics.en_US
dc.language.isoenen_US
dc.rightsCopyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.
dc.subjectBayesian P-splineen_US
dc.subjectCOVID-19en_US
dc.subjectCross-sectional studyen_US
dc.subjectReproduction numberen_US
dc.subjectSARS-CoV-2en_US
dc.titleAppropriately smoothing prevalence data to inform estimates of growth rate and reproduction number.en_US
dc.typeArticleen_US
dc.identifier.eissn1878-0067
dc.identifier.journalEpidemics 2022;40:100604en_US
dc.source.journaltitleEpidemics
dc.source.volume40
dc.source.beginpage100604
dc.source.endpage
dc.source.countryNetherlands


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