Evidence map›Paper›PMID 41021969›Full record

ArticleJMIR public health and surveillance2025

Comparison of Virus Watch COVID-19 Positivity, Incidence, and Hospitalization Rates With Other Surveillance Systems: Surveillance Study.

Wing Lam Erica Fong, Vincent Grigori Nguyen, Sarah Beale, Thomas E Byrne, Cyril Geismar, Ellen Fragaszy, Jana Kovar, Alexei Yavlinsky, Ibrahim Abubakar, Andrew C Hayward and 1 more

Abstract readComparative Study
In one paragraph

Article in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Wing Lam Erica FongInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0001-5097-2228
Vincent Grigori NguyenInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0002-9776-6242
Sarah BealeInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0002-4038-7460
Thomas E ByrneInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0001-8667-0870
Cyril GeismarNIHR Health Protection Research Unit in Modelling and Health Economics, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.ORCID 0000-0002-8486-5890
Ellen FragaszyInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0002-0178-6098
Jana KovarInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0002-0477-2767
Alexei YavlinskyInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0002-4100-4257
Ibrahim AbubakarFaculty of Population Health Sciences, University College London, London, United Kingdom.ORCID 0000-0002-0370-1430
Andrew C HaywardInstitute of Epidemiology and Health Care, University College London, London, United Kingdom.ORCID 0000-0002-3549-6232
Robert W AldridgeInstitute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 02035495969.ORCID 0000-0003-0542-0816

Funding

Medical Research Council MC_PC 19070Medical Research Council MR/V028375/1Wellcome Trust
6 · The paper itself

Abstract

Background: Effective disease surveillance is essential for understanding pathogens' epidemiology, detecting outbreaks, and enabling timely public health responses. In the United Kingdom, large-scale studies, such as the Office for National Statistics COVID-19 Infection Survey (CIS), have monitored SARS-CoV-2 transmission but required significant resources, making them challenging to sustain when pandemic-specific funding ends and also in resource-limited settings. In contrast, the Virus Watch study, at lower cost, relied on self-reported and linked national testing data as well as symptomatic testing, while Severe Acute Respiratory Infections Watch (SARI) leveraged hospital data for cost-effective surveillance. Objective: This study aimed to evaluate the effectiveness of Virus Watch as a surveillance system in monitoring COVID-19 positivity, incidence, and hospitalization rates in England and Wales, using data from the CIS and SARI as benchmarks for comparison, while considering the key differences in the study designs, including recruitment strategies, incentives, and testing criteria. Methods: We used the Virus Watch prospective community cohort study to estimate COVID-19 positivity, incidence, and hospitalization rates in England and Wales from June 2020 to March 2023. Rate estimates were compared with CIS modeled positivity and incidence rates, and with SARI COVID-19 hospitalization rates. Global synchrony between datasets was measured using overall Spearman ⍴ and local synchrony using 9-week rolling Spearman ⍴. For England, comparisons with CIS estimates used Virus Watch rates calculated with and without linked national testing data. Positivity rates were also assessed overall and separately before and after the end of free national testing. Results: A total of 58,628 participants were recruited into the Virus Watch study, of whom 52,526 (89.6%) were resident in England and 1532 (2.6%) in Wales; region was missing for the remainder. Virus Watch-estimated COVID-19 positivity and incidence rates in England, calculated with and without linked testing data, showed strong global synchrony with CIS estimates (positivity ⍴: 0.91 and 0.90; both P<.001 and incidence ⍴: 0.92 and 0.90; both P<.001) and strong local synchrony (positivity ⍴: median 0.75, IQR 0.53-0.85 and median 0.67, IQR 0.47-0.83, and incidence ⍴: median 0.76, IQR 0.49-0.88 and median 0.66, IQR 0.45-0.82), despite having lower absolute values. Global and local synchrony of positivity rates were similar for periods before and after the end of free national testing, although the difference between Virus Watch and CIS estimates was greater post-free testing. COVID-19 hospitalization rates were also lower and less synchronized with SARI estimates. In Wales, Virus Watch estimates exhibited greater variability (positivity ρ: 0.75, P<.001; incidence rate ρ: 0.85, P<.001) and lower local synchrony (positivity ρ: median 0.61, IQR 0.34-0.74, and incidence ρ: median 0.52, IQR 0.38-0.71) compared to England. Conclusions: Our results highlight the effectiveness of the Virus Watch approach in providing accurate estimates of COVID-19 positivity and incidence rates, even in the absence of national surveillance systems. This low-cost method can be adapted to various settings, particularly low-resource ones, to strengthen public health surveillance and inform timely interventions.

Indexed as

COVID-19HospitalizationPopulation SurveillanceAdultEnglandFemaleHumansIncidenceMaleMiddle AgedProspective StudiesSARS-CoV-2WalesCOVID-19public health monitoringSARS-CoV-2study comparisonsurveillance

Identifiers

PMID41021969
PMCPMC12479050

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.