Pritchard, Emma;
Vihta, Karina;
Eyre, David W;
Hopkins, Susan;
Peto, Tim EA;
Matthews, Philippa;
Stoesser, Nicole;
... COVID-19 Infection Survey Team; + view all
(2024)
Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.
American Journal of Epidemiology
, 193
(12)
pp. 1848-1860.
10.1093/aje/kwae091.
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Abstract
Detecting and quantifying changes in the growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers’ rationale for implementing or continuing interventions aimed at reducing their impact. Substantial changes in SARS-CoV-2 prevalence with the emergence of variants have provided an opportunity to investigate different methods for doing this. We collected polymerase chain reaction (PCR) results from all participants in the United Kingdom’s COVID-19 Infection Survey between August 1, 2020, and June 30, 2022. Change points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalized additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8 799 079 study visits, 147 278 (1.7%) were PCR-positive. Change points associated with the emergence of major variants were estimated to occur a median of 4 days earlier (IQR, 0-8) when using GAMs versus ISR. When estimating recent change points using successive data periods, 4 change points (4/96) identified by GAMs were not found when adding later data or by ISR. Change points were detected 3-5 weeks after they occurred under both methods but could be detected earlier within specific subgroups. Change points in growth rates of SARS-CoV-2 can be detected in near real time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories, both methods could be used in parallel.
| Type: | Article |
|---|---|
| Title: | Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/aje/kwae091 |
| Publisher version: | https://doi.org/10.1093/aje/kwae091 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | change-point detection, SARS-CoV-2 infection, community surveillance, real-time monitoring |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174474 |
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