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Researchers use mobile device data to predict COVID-19 outbreaks

January 23, 2022
by Colin Poitras

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 data analysis applied in the study could help health officials stem community outbreaks of COVID-19 in the future and allocate testing resources more efficiently, the researchers said.

The research was a collaborative effort involving data scientists and epidemiologists from YSPH, 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 YSPH 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 such analysis can be flawed.

“Mobility metrics often measure distance traveled or time spent away from a location, such as your home,” Crawford explained. “But we all know it’s possible to move around a lot and still not get very close to other people. So mobility metrics are not a great proxy for transmission risk. We feel close contact predicts infections and local outbreaks better.”

The findings are based on a review of Connecticut mobile device geolocation data from February 2020 to January 2021. All of the data was anonymized and aggregated, and no personally identifiable information was collected. The data were collected as part of a contractual agreement between Whitespace Ltd. and the Connecticut Department of Public Health. Crawford and the team from YSPH provided analytical support as part of a separate contract with the state agency.

A novel algorithm computed the probability of close contact events across the state (times when mobile devices were within six feet of each other) based on the geolocation data. That information was then incorporated into a standard COVID-19 transmission model to predict COVID-19 case levels not only across Connecticut, but in individual Connecticut towns, census tracts and census block groups.

This effort gave Connecticut epidemiologists and policymakers insight to people’s social distancing behavior statewide.

Forrest Crawford

The researchers said they successfully predicted an initial wave of Connecticut COVID-19 cases from March to April 2020, a drop in statewide cases during June to August and localized outbreaks in certain Connecticut towns in August and September.

Many health officials currently rely on general surveillance data such as the number of confirmed cases, hospitalizations and deaths to track the spread of COVID-19. But that process can lag actual disease transmission by days and weeks. Close personal contact rates provide faster results, the researchers said.

“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,” Crawford said.

He praised the analytical approach used in the study.

“Statisticians often analyze imperfect data collected under less-than-ideal circumstances,” Crawford said. “In this project, we were able to define clearly what we wanted to measure, implement a large-scale project to collect the data, analyze it to get epidemiological insights and then deliver these insights to policymakers.”

The research is a great example of how academia, the private sector and government policymakers can work closely together to achieve a common goal, he said.

Contributing authors on the study were: YSPH Associate Professor of Biostatistics Joshua Warren, Ph.D; YSPH Associate Clinical Professor Dr. Matthew Cartter, M.D., M.P.H., state epidemiologist for the Connecticut Department of Public Health; Sydney Jones, Ph.D., of the Epidemic Intelligence Service of the CDC and Infectious Disease Section of the Connecticut Department of Health; Assistant Professor Zehang Richard Li, Ph.D., of the University of California Santa Cruz (a former postdoc in the YSPH Biostatistics department) and current YSPH Biostatistics Ph.D. student Samantha Dean.

Members of the Whitespace Ltd. team listed as co-authors on the study were: Jacqueline Barbieri, CEO of Whitespace; Jared Campbell, Patrick Kenney and Thomas Valleau. Assistant Professor Olga Morozova, Ph.D., of the Program in Public Health and Department of Family, Population and Preventative Medicine at Stony Brook University is senior author. Morozova is a former Ph.D. student at YSPH and a former postdoc in the YSPH Biostatistics department.

Submitted by Colin Poitras on January 22, 2022