Evidence map›Paper›PMID 42118714›Full record

ArticlePLoS medicine2026

Social contact patterns in the United Kingdom following the COVID-19 pandemic: The Reconnect cross-sectional survey.

Lucy Goodfellow, Billy J Quilty, Kevin van Zandvoort, W John Edmunds

Abstract read
In one paragraph

Article in PLoS medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

4 authors.

Lucy GoodfellowCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0009-0004-0434-5863
Billy J QuiltyCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Kevin van ZandvoortCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-7770-0537
W John EdmundsCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-9179-2917

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClose-contact and respiratory infectious diseases are spread through social interactions. Measuring these interactions has transformed our ability to understand transmission and control these infections. Social contact patterns were disrupted during the COVID-19 pandemic and have been affected by wider demographic, cultural, and workplace changes since then. METHODS AND

findingsTo estimate post-pandemic social contact patterns in the United Kingdom, we conducted a cross-sectional social contact survey from November 2024 to March 2025 on a nationally representative sample of participants. Interactions were captured by age, gender, and across socioeconomic status (SES) and ethnic groups. We calculated the mean number of daily contacts and contact matrices, stratified by variables of interest, using a negative binomial regression model weighted by age, gender, ethnic group, and weekday/weekend. 13,238 participants were recruited, 3,019 of whom were aged under 18 years old; survey response rates were 36% and 27% for adults and children, respectively. The mean number of daily contacts was 9.1 (95% confidence interval (CI): 8.7, 9.5); this figure was 13.8 (95% CI: 12.8, 14.9) for children, and 7.8 (95% CI: 7.4, 8.2) for adults. Higher numbers of contacts were positively associated with employment, household income, and educational qualifications held. Contact matrices showed high levels of age-assortativity, as well as inter-generational contacts in the home. Contacts were assortative between ethnic groups and SES in all settings; this effect was strongest between ethnic groups in the home, and between SES in the workplace. We constructed socially-stratified next-generation matrices for a novel respiratory pathogen, projecting that the majority White ethnic group would account for the largest share of new infections (76.7% (95% CI: 75.5, 77.9) of cases), but that per-capita infection risk would disproportionately affect minority ethnic groups, with the risk for the Black population being 2.27 (95% CI: 2.06, 2.51) times that of the White population. This study may be limited by the inherent recall biases and reporting fatigue involved with self-reporting contacts.

conclusionsThis study provides crucial data to inform post-pandemic mathematical models of infectious disease transmission, and allows ethnicity and SES to be incorporated in such models.

Indexed as

COVID-19Social InteractionAdolescentAdultAgedChildChild, PreschoolContact TracingCross-Sectional StudiesFemaleHumansInfantMaleMiddle AgedPandemicsSARS-CoV-2

Identifiers

PMID42118714
PMCPMC13166901

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.