Communications of the ACM
Researchers Use Mobile Device Data to Predict COVID-19 Outbreaks
Yale School of Public Health associate professor Forrest Crawford said, "This effort gave Connecticut epidemiologists and policymakers insight to people's social-distancing behavior statewide."
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Yale School of Public Health (YSPH) researchers used anonymous location information from mobile devices to accurately forecast COVID-19 outbreaks in Connecticut municipalities.
Said YSPH's Forrest Crawford, "We measured close interpersonal contact within a six-foot radius everywhere in Connecticut using mobile-device geolocation data over the course of an entire year," an effort that "gave Connecticut epidemiologists and policymakers insight to people's social-distancing behavior statewide."
A novel algorithm was used to calculate the likelihood of times when mobile devices were within six feet of each other, according to the geolocation data.
This information was embedded within a standard COVID-19 transmission model to predict case levels statewide, and in individual towns, census tracts, and census block groups.
Said Crawford, “The contact rate we developed in this study can reveal high-contact conditions likely to spawn local outbreaks and areas where residents are at high transmission risk days or weeks before the resulting cases are detected through testing, traditional case investigations and contact tracing.”
From Yale School of Public Health
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