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Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number

Title: Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number
Authors: Eales, O
Ainslie, KEC
Walters, CE
Wang, H
Atchison, C
Ashby, D
Donnelly, CA
Cooke, G
Barclay, W
Ward, H
Darzi, A
Elliott, P
Riley, S
Item Type: Journal Article
Abstract: The time-varying reproduction number () 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 from case data. However, these are not easily adapted to point prevalence data nor can they infer 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 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 over shorter periods tracking a temporary increase above one during late-May 2020, a gradual increase in over the summer of 2020 as restrictions were eased, and a reduction in 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.
Issue Date: 1-Sep-2022
Date of Acceptance: 17-Jun-2022
URI: http://hdl.handle.net/10044/1/99415
DOI: 10.1016/j.epidem.2022.100604
ISSN: 1755-4365
Publisher: Elsevier
Journal / Book Title: Epidemics: the journal of infectious disease dynamics
Volume: 40
Copyright Statement: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/)
Sponsor/Funder: Department of Health
Department of Health
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: n/a
n/a
RDF03
Keywords: Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
SARS-CoV-2
COVID-19
BayesianP-spline
Cross-sectionalstudy
Reproductionnumber
SARS-COV-2
EPIDEMIC
Bayesian P-spline
COVID-19
Cross-sectional study
Reproduction number
SARS-CoV-2
Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
SARS-CoV-2
COVID-19
BayesianP-spline
Cross-sectionalstudy
Reproductionnumber
SARS-COV-2
EPIDEMIC
1103 Clinical Sciences
1117 Public Health and Health Services
Publication Status: Published
Article Number: ARTN 100604
Appears in Collections:Department of Surgery and Cancer
Department of Infectious Diseases
Institute of Global Health Innovation
School of Public Health



This item is licensed under a Creative Commons License Creative Commons