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Thursday, December 03, 2020

Explained: Models throw light on venues triggering most COVID-19 infections

Authors of the study say the results can guide policymakers to assess different approaches to reopening certain establishments by estimating the trade-off between new infections and visits lost from reopening.

By: Explained Desk | Updated: November 15, 2020 12:37:23 pm
In this Nov. 11, 2020, file photo, patrons enjoy food and drink at The Brass Rail in Hoboken, N.J. The model says venues such as restaurants, cafes and gyms account for most Covid-19 infections in ten US cities. (AP Photo/Seth Wenig, File)

A new computer model derived from the mobile data of over 100 million people in the US published in the journal Nature on November 10 says venues such as restaurants, cafes and gyms account for most COVID-19 infections in ten US cities. The model has been developed by researchers from Stanford and Northwestern University.

Authors of the study say the results can guide policymakers to assess different approaches to reopening certain establishments by estimating the trade-off between new infections and visits lost from reopening.

So, what is the computer model?

Using geolocation data from mobile phones, the researchers created a model to track the spread of COVID-19 within ten of the largest metropolitan areas in the US, which include New York, Los Angeles, Chicago, Dallas, Washington D.C., Houston, Atlanta, Miami, Philadelphia and San Francisco. 📣 Click to follow Express Explained on Telegram

The model tracked the hourly movements of over 98 million people from census block groups (CBGs) — geographical units that typically contain between 600-3,000 people — to specific points of interest (POIs), which are non-residential locations such as restaurants, grocery stores and religious establishments.

Explained | As second Covid-19 wave hits, should indoor dining be allowed when classrooms close

What does it tell us?

By recording information about individual POIs such as the hourly number of visitors, median visit distribution, the researchers’ computer model can help to estimate the impact of some reopening strategies.

In their research, the authors note their model does not include all real-world features relevant to disease transmission. However, the predictive accuracy of their model captures the relationship between mobility and transmission from which they have drawn broad conclusions, such as that people from lower-income CBGs have higher infection rates since they tend to visit denser POIs and because they have not reduced mobility as much, possibly because it is more difficult for them to work from home.

Further, their model also predicts that in every metro area, people in non-white CBGs areas are likelier to be infected, a disparity that is driven by a few POI categories such as full-service restaurants.

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