Did COVID-19 surveillance system sensitivity change after Omicron? A retrospective observational study in England

Brainard, Julii, Lake, Iain R., Morbey, Roger A., Elliot, Alex J. and Hunter, Paul R. (2025) Did COVID-19 surveillance system sensitivity change after Omicron? A retrospective observational study in England. BMC Infectious Diseases. ISSN 1471-2334 (In Press)

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Abstract

Background: During the COVID-19 pandemic in England, increases and falls in COVID-19 cases were monitored using many surveillance systems (SS). However, surveillance sensitivity may have changed as different variants were introduced to the population, due to greater disease-resistance after comprehensive vaccination programmes and widespread natural infection or for other reasons.   Methods: Time series data from ten epidemic trackers in England that were available Sept 2021-June 2022 were compared to each other using Spearman correlation statistics. Least biased and most timely SS in England were identified as ‘best’ standard epidemic trackers, while other COVID-19 tracking datasets we denote as complementary trackers. We compared the best standard trackers with each other and with the complementary trackers. Correlation calculations with 95% confidence intervals were made between complementary and best standard epidemic trackers. We tested the hypothesis that correlation with the best trackers was especially poor during transition periods when Delta, Omicron BA.1 and Omicron BA.2 sublineages were each dominant. Daily ascertainment percentages of incident cases that each SS detected during each variant’s dominance were calculated. We tested for statistically significant (at p < 0.05) differences in the distribution of the ascertainment values during each COVID-19 variant’s dominance, using Welch’s oneway ANOVA.   Results: Spearman rho correlation was significantly positive between most complementary and the best trackers over the whole period. There was no apparent visual indication that correlations were especially poor during transition period from Delta to BA.1. There were falls in correlation in the transition period from BA.1 to BA.2 but these falls were relatively small compared to correlation fluctuations over the full period. Ascertainment was highest in the Delta period for complementary systems against the least biased tracker of incidence. Ascertainment was statistically different between the three variant-dominant periods.   Conclusions: From September 2021 to June 2022, complementary SS generally reflected case rises and falls. Ascertainment was highest in the Delta-dominant period but no complementary tracker was highly stable. Factors other than which variant was dominant seem likely to have affected how well each tracker reflected true case rises and falls.

Item Type:	Article
Additional Information:	Availability of data and materials: None of the original datasets were collected by ourselves and therefore they should not be redistributed by us. Zoe application, ONSCISE, COVID-19 hospital admissions and Pillar 1 & 2 data all have been accessible in the public domain. Applications for requests to access relevant anonymised data included in this study held by UKSA should be submitted to the UKHSA Office for Data Release at: https://www.gov.uk/government/publications/accessing-ukhsa-protected-data/accessing-ukhsa-protected-data ). R scripts used for analysis and to generate plots are available at https://github.com/JuliiBrainard/SpotOmicron/tree/main. Funding information: This work was funded and authors were financially renumerated by the National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Emergency Preparedness and Response at King’s College London in partnership with the UK Health Security Agency (UKHSA) in collaboration with the University of East Anglia. AJE is also affiliated with the NIHR HPRU in Gastrointestinal Infections at the University of Liverpool. The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR, UEA, any HPRU, Department of Health or UKHSA. The funders had no role in writing the manuscript. Authors were not precluded from accessing data that supported this study and authors accept responsibility for this submission.
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health Faculty of Science > Research Groups > Environmental Social Sciences University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Social Sciences > Research Centres > Water Security Research Centre Faculty of Medicine and Health Sciences > Research Centres > Population Health
Depositing User:	LivePure Connector
Date Deposited:	15 May 2025 13:30
Last Modified:	19 May 2025 00:17
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99281
DOI:

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