Abstract
Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.
Original language | English |
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Article number | 4164 |
Journal | Nature Communications |
Volume | 15 |
Number of pages | 17 |
ISSN | 2041-1723 |
DOIs | |
Publication status | Published - 16 May 2024 |
Bibliographical note
Funding Information:We thank the entire Seattle Flu Study (SFS) and Greater Seattle Coronavirus Assessment Network (SCAN) team for their hard work and dedication to these projects and the study participants for their participation in this research. We also thank Public Health \u2013 Seattle & King County for their contributions to the SCAN study and for providing samples collected at King County COVID-19 drive-through testing sites, the Tacoma-Pierce County Health Department for funding the collection and testing of SCAN respiratory specimens in Pierce County, and the Rapid Health Information Network (RHINO) program at the Washington Department of Health for providing syndromic surveillance data. We thank Dr. Jeff Duchin for helpful comments on the manuscript and the Division of International Epidemiology and Population Studies (DIEPS) of the Fogarty International Center and the Bedford Lab at Fred Hutch for useful discussions. This study used the high-performance computational resources of the Biowulf Linux cluster at the US National Institutes of Health ( http://biowulf.nih.gov ). Funding for the Seattle Flu Study and Greater Seattle Coronavirus Assessment Network (SCAN) was provided by Gates Ventures and the Howard Hughes Medical Institute. SCAN samples collected in Pierce County were funded by the Tacoma-Pierce County Health Department. ACP, CLH, SB, RP, CM, DR, BC, KSF, KK, BP, ZA, EM, LRS, JSt, LG, PDH, AW, JSh, TB, HYC, and LMS received third-party support from Gates Ventures through the Brotman Baty Institute during the conduct of the study. ACP, LMS, and TB are supported by CDC contract 75D30122C14368. RB and MF are employees of the Institute for Disease Modeling, a research group within and solely funded by the Bill and Melinda Gates Foundation. JSh and TB are supported by the Howard Hughes Medical Institute. CV is supported by the in-house research division of the Fogarty International Center, US National Institutes of Health. For samples collected through mechanisms other than SCAN, the funders had no role in any aspect of the study. Gates Ventures participated in the design of SCAN by providing input on the study screener and eligibility criteria but had no role in the conduct of SCAN, the collection, management, analysis, or interpretation of SCAN data, the preparation, review, or approval of this manuscript, or the decision to submit the manuscript for publication. No other funders were involved in any aspect of SCAN. Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US National Institutes of Health or the US government