COVID-19の間の接触パターンを理解する

オリジナルソース

タイトル: Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey

概要: BackgroundContact 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 Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US.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 time, spatial scales, and demographic and social classifications, and what social behavior looks like at baseline in the absence of an ongoing pandemic. MethodsWe analyze spatiotemporal non-household contact patterns across 10.7 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 non-pandemic US contact data, we employ a regression approach to estimate baseline contact and address this gap. FindingsAlthough 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. InterpretationWe develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing future disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory infectious disease transmission in the US. FundingResearch reported in this publication was supported by the National Institutes of Health under award number R01GM123007 and R35GM153478 (SB). Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched Google Scholar for contact data in the US both during and prior to the pandemic published by February 1, 2024 with the search terms "contact patterns", "social contact data", "disease-relevant contacts", "change in contacts pandemic", "urban rural social contacts," and "seasonality in contact patterns". We reviewed the bibliographies of these articles and included known literature not found via these search criteria. We excluded studies using mobility data, focusing on children, or excluding the United States. Previous work has been limited to the state scale or subsets of counties (e.g., focused on a few cities, a single state, or a few counties within a state) rather than all counties in the US. Added value of this studyWe contribute the first high-resolution pandemic contact estimates for the US and infer non-pandemic contact patterns at fine spatial and temporal scales. Our results indicate that the number of contacts is fairly stable over time in the absence of major disease, suggesting that the number of contacts is not a primary driver of respiratory infectious disease seasonality in the US. We also identify groups at greatest disease risk due to higher contacts, including younger adults, men, and Hispanic and Black individuals. Implications of all the available evidenceThis study demonstrates the importance of incorporating age-specific and spatial heterogeneity of contact patterns into future disease models to build accurate estimates of transmission risk. We demonstrate that temporal variability in contact patterns is insufficient to drive respiratory infectious disease seasonality, that adaptive behaviors in response to disease shift risk along an urban-rural gradient, and that some vulnerable groups are at increased risk of exposure due to contact. We advocate that geographic and social heterogeneity in exposure to disease due to contact patterns be captured more comprehensively for accurate infectious disease predictions and effective and equitable disease mitigation.