Background: Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across spatial scales and demographic and social classifications, whether there is seasonality to contact patterns, and what social behavior looks like at baseline in the absence of an ongoing pandemic. Methods: We analyze spatiotemporal non-household contact patterns across 11 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of pre-pandemic data on contact in the US, we also use a regression approach to produce baseline contact estimates to fill this gap. Findings: Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of white individuals and contacts at work or social events change the most under increased national incidence. Interpretation: We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory disease transmission in the US.

The authors have declared no competing interest.

Research reported in this publication was supported by the National Institutes of Health under award number R01GM123007 (SB).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was reviewed by the Institutional Review Board at Georgetown University and was determined not to be human subjects research.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Contact estimates, both during the pandemic and at baseline, at the county-week scale and all code to produce and analyze these data will be made available on GitHub at https://github.com/bansallab/resp_contact. Individual survey responses cannot be shared by the authors, but researchers can refer to https://cmu-delphi.github.io/delphi-epidata/symptom-survey/data-access.html if they would like to enter an agreement for data usage with CMU Delphi.

https://github.com/bansallab/resp_contact