Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data

Abstract

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.

Figure 1:

Schematic illustration of contact probability calculation. From left to right: raw locations, including horizontal uncertainty estimates, for two mobile devices are transformed into approximate location probability densities. The distribution of distances from points drawn randomly from these densities is computed. Sampled distances are shown here for illustrative purposes in red (when sampled device locations are within six feet apart) and gray (when sampled locations are more than six feet apart); in our implementation, the distribution of these distances is computed analytically. The shaded area under the density is the probability that the devices are within six feet.

Figure 2:

Estimated contact rate among mobile devices in our dataset in Connecticut from February 2020 to February 2021. At top, maps show the number of contacts in Connecticut’s 169 towns per day during weeks beginning on the first of each month. Darker colors indicate higher contact. At bottom, statewide contact shows the daily frequency of close contact within six feet between distinct devices in our dataset. Governor Ned Lamont’s stay-at-home order and reopening phases 1, 2, 3, and 2.1 indicated. The state reverted to the more restrictive “Phase 2.1” in response to rising case counts in November.

Figure 3:

Contact rates, COVID-19 cases, and model predictions (with 95% uncertainty intervals) of infections, cases, and cumulative incidence proportion in the five largest cities by population in Connecticut: Bridgeport, New Haven, Hartford, Stamford, and Waterbury. Black dots show confirmed non-congregate COVID-19 case counts.

Figure 4:

Contact rates, COVID-19 cases, and model predictions (with 95% uncertainty intervals) of infections, cases, and cumulative incidence proportion in several towns in Connecticut whose case or contact patterns differ from that of the state as a whole: Danbury, Fairfield, Norwich, Old Saybrook, and Waterford. Public health officials declared an outbreak in Danbury in mid-August 2020. Fairfield experienced outbreaks linked to two universities in September 2020. Norwich, Old Saybrook, and Waterford, in the eastern part of the state, were mostly spared during the first wave of infection, and had quickly rising case counts in fall 2020.