Evidence map›Paper›PMID 40866283›Full record

ArticleThe Lancet. Digital health2025

Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey.

Juliana C Taube, Zachary Susswein, Vittoria Colizza, Shweta Bansal

Abstract read
In one paragraph

Article in The Lancet. Digital health, 2025. 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.

  1. Current practice and timing of rapid point-of-care testing for acute respiratory tract infections in outpatient care: a retrospective cohort study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Juliana C TaubeDepartment of Biology, Georgetown University, Washington, DC, USA.
Zachary SussweinDepartment of Biology, Georgetown University, Washington, DC, USA.
Vittoria ColizzaDepartment of Biology, Georgetown University, Washington, DC, USA; INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.
Shweta BansalDepartment of Biology, Georgetown University, Washington, DC, USA. Electronic address: shweta.bansal@georgetown.edu.

Funding

Vaccine hesitancy and erosion of herd immunity: harnessing big data to forecast disease re-emergenceR01GM123007 · NIGMS · GEORGETOWN UNIVERSITY · PI BANSAL, SHWETA · 2017 to 2021
$2.1M
Integrating opportunistic data into respiratory disease models to enhance surveillance, explain seasonality, and reveal spatial transmission landscapesR35GM153478 · NIGMS · GEORGETOWN UNIVERSITY · PI Shweta Bansal · 2024 to 2026
$1.3M
NIGMS NIH HHS R01 GM123007NIGMS NIH HHS R35 GM153478
6 · The paper itself

Abstract

backgroundInterpersonal contact has a crucial role in the transmission of infectious diseases. Characterising heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict differences in vulnerability by age, and inform physical distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and is potentially unrepresentative of behaviour in other geographical regions. We aimed to understand the variation in contact patterns in the USA across time, spatial scales, and demographic and social classifications during the COVID-19 pandemic, and to estimate what social behaviour looks like at baseline, in the absence of an ongoing pandemic.

methodsFor this study of contact patterns relevant to respiratory transmission during a pandemic, we examined 10·7 million responses to the US COVID-19 Trends and Impact Survey between June 1, 2020, and April 30, 2021 (ie, during the COVID-19 pandemic); the survey recruited participants aged 18 years and older in the USA through Facebook. Data were post-stratified by age and gender to correct for sample representation. We used generalised additive models to characterise spatiotemporal heterogeneity in respiratory contact patterns during the pandemic at the county-week scale; we established how contact patterns vary by urbanicity, age (18-54 years, 55-64 years, 65-74 years, or ≥75 years), gender (male or female), race or ethnicity (Asian, Black or African American, Hispanic, White, or other), and contact setting (work, shopping for essentials, social gatherings, or other). We used a regression approach to estimate baseline (non-pandemic) contact patterns.

findingsAlthough contact patterns varied over time during the COVID-19 pandemic, the average number of daily contacts was relatively stable after controlling for the effect of incidence-mediated risk perception and disease-related policy. The mean number of non-household contacts was spatially heterogeneous, varying across urban versus rural settings, regardless of the presence of disease. Additional heterogeneity was observed across age, gender, race or ethnicity, and contact setting. Mean number of contacts decreased with age for individuals older than 55 years and was lower in women than in men. During periods of increased national incidence of disease, the contacts of White individuals and contacts at work or social gatherings showed the greatest change.

interpretationOur findings indicate that US adult baseline contact patterns show little variability over time after controlling for disease, but high spatial variability regardless of disease, with implications for understanding the seasonality of respiratory infectious diseases. The highly structured spatiotemporal, demographic, and social heterogeneity in contact patterns reported here could inform the risk landscape of respiratory infectious disease transmission in the USA and the implementation of targeted interventions, and our county-level estimates of non-pandemic contact rates could fill gaps in parameterising future disease models.

fundingNational Institutes of Health, Agence Nationale de la Recherche, and EU Horizon Europe.

Indexed as

COVID-19AdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUnited StatesYoung Adult

Identifiers

PMID40866283
PMCPMC12447159

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LicenceCC BY-NC-ND
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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.