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A computer model using cell phone data to map the places people frequent each day in major cities may indicate that most COVID-19 infections occur at “high-profile” sites such as service restaurants full, gyms and cafes.
The report, published Tuesday in the journal Nature, examined data from 98 million Americans collected in 10 major US cities, including San Francisco, for two months starting in March. The data was then fed into an epidemiological model developed by a team led by Stanford University.
Jure Leskovec, the Stanford computer scientist who led the study, told Stanford News that the model analyzed how people from different demographics and from different neighborhoods visited more or less crowded establishments.
“Based on all of this, we could predict the likelihood of new infections at some place or time,” he said.
These predictions will later prove to be correct on the basis of the number of infections officially recorded by the cities.
The scientists used data provided by SafeGraph, a Denver-based company that aggregates anonymized location information from cell phone apps to track which public places they visit each day and the length of their stay. The square footage of each establishment was recorded to determine the hourly occupancy density.
Various scenarios the model simulated, including the reopening of some businesses but not others, showed that opening restaurants to full capacity led to the greatest increase in infections. Gyms came second, followed by cafes and hotels / motels. According to one scenario, if the capacity was limited to 20% at all sites, new infections would be reduced by more than 80%.
When combined with demographic information from the census, the data also suggests why people in poor neighborhoods are more likely to contract COVID-19:
—They are less able to work from home.
—Stores where they buy essential supplies tend to be more crowded than in more affluent areas.
—They stay in these stores longer (about 17% more on average) than those in high-income areas.
The findings could help cities develop strategies to contain the spread of COVID-19 while limiting the damage to their economies.
However, two scientists from the University of Oxford said more research was needed to test whether the model correctly identified the true location of infections.
Christopher Dye, an epidemiologist at the university, told Nature that the research, while promising, is “an epidemiological hypothesis” that needs to be validated with real-world data. Moritz Kraemer, who models infectious diseases at Oxford, said more detailed tracing of contracts is required.
The study builds on previous efforts using computer models to predict how the virus spreads in enclosed spaces. For example, at the University of Colorado, Boulder, atmospheric chemist José Luis Jiménez developed a tool called COVID Airborne Transmission Estimator, which analyzes the risk of indoor transmission of the virus via aerosols under various conditions at home, in businesses like bars and restaurants; and in classrooms.
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