Impact of Close Interpersonal Contact on COVID-19 Incidence: Evidence from One Year of Mobile Device Data

• Activity Number: 119 - Statistics for Mobile and Wearable Device Data
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Forrest W. Crawford* and Sydney A. Jones and Matthew Cartter and Samantha Dean and Joshua L. Warren and Zehang Richard Li and Jacqueline Barbieri and Jared Campbell and Patrick Kenney and Thomas Valleau and Olga Morozova
• Companies: Yale University and CDC/CT DPH and CT DPH and Yale School of Public Health and Yale School of Public Health and UCSC and Whitespace Ltd and Whitespace Ltd and Whitespace Ltd and Whitespace Ltd and Stony Brook University
• Keywords: COVID-19; epidemic; mobile device; GPS; geo-location

Abstract

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes COVID-19. We quantified interpersonal contact at the population-level using mobile device geolocation data by computing the frequency of contact (within six feet) between people in Connecticut during February 2020 -- January 2021. 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 SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact in Connecticut explains the large initial wave of infections during March–April 2020, 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.