Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.
| العنوان: | Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data. |
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| المؤلفون: | Crawford FW; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.; Department of Statistics & Data Science, Yale University, New Haven, CT, USA.; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Yale School of Management, New Haven, CT, USA., Jones SA; Epidemic Intelligence Service, Centers for Disease Control & Prevention, Atlanta, GA, USA.; Infectious Diseases Section, Connecticut Department of Public Health, New Haven, CT, USA., Cartter M; Infectious Diseases Section, Connecticut Department of Public Health, New Haven, CT, USA., Dean SG; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA., Warren JL; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA., Li ZR; Department of Statistics, University of California, Santa Cruz, Santa Cruz, CA, USA., Barbieri J; Whitespace Solutions, Ltd, Alexandria, VA, USA., Campbell J; Whitespace Solutions, Ltd, Alexandria, VA, USA., Kenney P; Whitespace Solutions, Ltd, Alexandria, VA, USA., Valleau T; Whitespace Solutions, Ltd, Alexandria, VA, USA., Morozova O; Program in Public Health and Department of Family, Population and Preventive Medicine, Stony Brook University, NY, USA. |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2021 Mar 12. Date of Electronic Publication: 2021 Mar 12. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation. One Sentence Summary: Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic. |
| التعليقات: | Update in: Sci Adv. 2022 Jan 07;8(1):eabi5499. (PMID: 34995121) |
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| معلومات مُعتمدة: | DP2 HD091799 United States HD NICHD NIH HHS; R01 AI137093 United States AI NIAID NIH HHS |
| تواريخ الأحداث: | Date Created: 20210324 Latest Revision: 20240216 |
| رمز التحديث: | 20240216 |
| مُعرف محوري في PubMed: | PMC7987027 |
| DOI: | 10.1101/2021.03.10.21253282 |
| PMID: | 33758869 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2021.03.10.21253282 |
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