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 mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the large initial wave of infections during March–April, the drop in cases during June–August, local outbreaks during August–September, broad statewide resurgence during September–December, and decline in January 2021. The transmission model exhibits a better fit to COVID-19 transmission dynamics using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

See detailed information in the preprint and supplement here: https://www.medrxiv.org/content/10.1101/2021.03.10.21253282v1

The data consist of three columns in a CSV file: date, town, and contact_rate. The date is in YMD format. The town is the town name in Connecticut. The contact rate is a non-negative number that represents the expected number of close interpersonal contact events in each town per day.