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Stanford University scientists’ computer model predicts the spread of COVID-19 in cities



A computer model using mobile phone data to map places where people frequently visit in big cities every day may indicate that most COVID-19 infections occur in “super spreader” locations, such as full-service restaurants, gyms, and cafes.

The report, published in the journal Nature on Tuesday, investigated data on 98 million Americans collected in 10 major cities in the United States, including San Francisco, in the two months beginning in March. The data was then fed into an epidemiological model developed by a team led by Stanford University.

Jure Leskovec, a computer scientist at Stanford University who led the research, told Stanford News that the model analyzes how people of different population backgrounds and neighborhoods visit more or less crowded places.


He said: “Based on all these, we can predict the possibility of a new infection at any given place or time.”

Based on the number of infections officially recorded by the city, these predictions will prove to be correct in the future.

The scientists used data provided by Denver-based SafeGraph, which aggregates anonymous location information from mobile apps to track which public places people visit every day and how long they stay there. Record the square footage of each site to determine the occupancy density per hour.

The various scenarios simulated by the model (including the reopening of some businesses but not reopening of others) show that opening up restaurants with full force will lead to the most increase in infections. The gym ranks second, followed by cafes and hotels/motels. According to one situation, if the capacity is limited to 20% in all locations, new infections will be reduced by more than 80%.

When combined with demographic information from the census, the data also shows why people from poor communities are more susceptible to COVID-19:

Their ability to work at home is weak.
-The shops where they buy necessities are more crowded than in the more affluent areas.
-They spend more time in those shops than in higher-income places (about 17% on average).

These findings can help cities develop strategies to contain the spread of COVID-19 while limiting the damage to the economy.

However, two scientists at Oxford University said that more research is needed to test whether the model correctly determines the actual location of the infection.

The university’s epidemiologist Christopher Dye told Nature that although the study is promising, it is an “epidemiological hypothesis” that needs to be verified with real-world data. It’s required.

This research is based on previous efforts made using computer models to predict how viruses will spread in enclosed spaces. For example, José Luis Jiménez, an atmospheric chemist at the University of Colorado at Boulder, has developed a tool called COVID Airborne Transmission Estimator, which analyzes indoor transmission through aerosols in businesses such as homes, bars and restaurants under various conditions. The chance of a virus. In the school classroom.




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