BETHESDA, Md. – Researchers at Stanford and Northwestern Universities have created models using anonymous cell phone data of Americans’ movement in 10 large metropolitan areas to predict that limiting capacity of indoor activities can significantly cut down on coronavirus infections.
READ MORE: US reports over 136K COVID-19 cases in new single-day record, according to Johns Hopkins
The study appears to confirm that the majority of COVID-19 infections happen at sites like full-service restaurants, fitness centers and cafes, with restaurants being about four times as risky as the other sites.
One of the researches, Jure Leskovec, a computer science professor at Stanford, tells FOX 5 the study shows cutting down on capacity inside can also prevent a large number of infections.
“If we were to reopen all places at 20 percent of maximium occupancy these places would lose about 40 percent of the traffic so it means they would still get lets say 60 percent of the visitors but the number of infections we would save would be 80 percent,” Leskovec said.
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In Montgomery County, County Executive Marc Elrich recently ordered restaurants and bars to decrease indoor capacity to 25 percent.
Several restaurants have told FOX 5 those kind of limits can make or break business.
READ MORE: Montgomery County implements tighter restrictions, asks state to do more with COVID-19 guidance
“For us at 25 percent capacity that’s 12 customers. I can’t pay my bills and pay my staff that way. We understand it’s the right thing to do and safe thing to do, but it’s catastrophic for us,” said Ashish Alfred, chef and owner of Duck Duck Goose in Bethesda.
Alfred says he has pivoted to encouraging outdoor dining with a BYOB (Bring Your Own Blanket) campaign.
“We really think that you’d just have a perfect night out if you just brought a blanket. It makes all the difference,” Alfred said.
The Stanford/Northwestern study used cell phone location data from March to May, but did not account for safety measures like masks.
The researchers say the model also accurately predicted higher rates of infections among minority and lower-income individuals based on differences in their mobility. The data show those groups can not limit their movement as much and visit more crowded and therefore higher-risk places.