Stanford Program Predicts Coronavirus Spread In CA Cities

CALIFORNIA — Stanford University Scientists have developed a computer model that uses cellphone location data to pinpoint COVID-19 infections at "superspreader" sites in large cities. The model found that full-service restaurants, gyms and cafes were among the riskiest places.

For two months, researchers tracked the movement of 98 million Americans in 10 major U.S. cities, including Los Angeles and San Francisco. The Stanford team used anonymized location data from mobile-phones, collected by SafeGraph, a Colorado based tech company.

As the model tracked participants everywhere from church to the car dealerships, researchers found that restaurants open at full capacity led to the largest increase in infections. Gyms, cafes and hotels followed close behind.

Jure Leskovec, the Stanford computer scientist who led the study, told Stanford News that the model "offers the strongest evidence yet" that stay-at-home orders issued by the state this spring slowed the rate of new infections.

Leskovec further explained how the model analyzed people of different demographic backgrounds and the neighborhoods they visited.

Their findings revealed that minorities and low-income households often left home more often because their jobs required it. Many lower-income participants also shopped at smaller, more crowded stores unlike those with higher incomes.

Those who made more money more than likely worked from home, used home delivery to shop for groceries and when they did go out, they patronized businesses that allowed for more space between customers.

Researchers found that grocery shopping, for instance, was riskier for non-white populations than whites.

"In the past, these disparities have been assumed to be driven by preexisting conditions and unequal access to health care, whereas our model suggests that mobility patterns also help drive these disproportionate risks," David Grusty, co-author of the study and professor of sociology at Stanford, told Stanford News.

The data was funneled into an epidemiological model developed by the Stanford team. Researchers analyzed this data from March 8 to May 9 in two phases. In the model, researchers developed a series of equations to determine the probability of infections occurring at different events, places and times.

Scientists were able to solve unknown variables after feeding the computer model how many coronavirus infections were reported in each city daily.

The team further used this model to simulate different scenarios, such as opening some restaurants or other businesses while keeping others closed. And the team is now working to transform the model for public health officials to use.

"Because the places that employ minority and low-income people are often smaller and more crowded, occupancy caps on reopened stores can lower the risks they face,” Grusky said. “We have a responsibility to build reopening plans that eliminate — or at least reduce — the disparities that current practices are creating."

Despite all of these findings, Christopher Dye, an epidemiologist at the University of Oxford, told the journal, Nature, that the study needed to be validated with more real-world data.

"It is an epidemiological hypothesis that remains to be tested. But it is a hypothesis that is well worth testing," Dye said.

The study was published in Nature, just weeks before California Gov. Gavin Newsom "pulled the emergency brake" on reopenings, issuing orders for a statewide curfew — from 10 p.m. to 5 a.m. — amid an alarming rate of COVID-19 community spread. And more than 94 percent of Californians are now living under the toughest COVID-19 restrictions, per the governor's orders over the last two weeks.

In Los Angeles County, where the study was partially conducted, public health officials pulled the trigger on tightening restrictions even further as cases exploded within the county. After 10 p.m. Wednesday, all restaurants and bars are ordered to halt all in-person dining for three weeks, pivoting to take-out and delivery service only as part of a new mandate from county officials.

California reported 15,329 cases of coronavirus Tuesday, adding to a total of 1,125,699 cases. Forty-three deaths were reported Tuesday, adding to the statewide death toll of 18,769.

Hospitals across the state have seen an 81.3 percent increase in coronavirus patients since the week of Nov. 10, health officials said Tuesday. And the number of patients admitted to intensive care units for COVID-19 shot up 57.1 percent within 14 days.

"We are truly in a surge here in California," said Dr. Mark Ghaly, the state's public health secretary, said at a Tuesday afternoon coronavirus briefing.

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