Researchers use mobile device data to predict COVID-19 outbreaks
Researchers at the Yale School of Public Health were able to accurately predict outbreaks of COVID-19 in Connecticut municipalities using anonymous location information from mobile devices, according to a new study published in Science Advances.
The novel analysis applied in the study could help health officials stem community outbreaks of COVID-19 and allocate testing resources more efficiently, the researchers said.
The study was conducted by data scientists and epidemiologists from the Yale School of Public Health, the Connecticut Department of Public Health, the U.S. Centers for Disease Control and Prevention and Whitespace Ltd., a spatial data analytics firm.
The key to the findings was the precision with which researchers were able to identify incidents of high frequency close personal contact (defined as a radius of 6 feet) in Connecticut down to the municipal level. The CDC advises people to keep at least six feet of distance with others to avoid possible transmission of COVID-19.
“Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes COVID-19,” said the study’s lead author Forrest Crawford, an associate professor of biostatistics at the Yale School of Public Health and an associate professor of ecology and evolutionary biology, management, statistics and data science at Yale.
“We measured close interpersonal contact within a 6-foot radius everywhere in Connecticut using mobile device geolocation data over the course of an entire year,” Crawford said. “This effort gave Connecticut epidemiologists and policymakers insight to people’s social distancing behavior statewide.”
Other studies have used so-called “mobility metrics” as proxy measures for social distancing behavior and potential COVID-19 transmission. But that analysis can be flawed. More