AI Article Synopsis
- Close contact between individuals is the main way SARS-CoV-2 spreads, and researchers used mobile geolocation data to analyze this in Connecticut from February 2020 to January 2021.
- The study measured how often people were within 6 feet of each other and used this information in a SEIR-type model to effectively predict COVID-19 case trends across different towns.
- Findings showed that contact rates were a better predictor of COVID-19 transmission dynamics than other mobility metrics, indicating that such data can inform social distancing measures and resource distribution for testing.
Article 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 quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to 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 initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741180 | PMC |
| http://dx.doi.org/10.1126/sciadv.abi5499 | DOI Listing |
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