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Officials contemplating reopening plans (or lockdowns) amid coronavirus could look to cell phone mobility data to better inform decisions, a new study suggests.

Researchers from Stanford University, among other institutions, studied anonymized data on 98 million people and their movement patterns hour-by-hour in the 10 largest metro areas in the U.S. An early version of the peer-reviewed findings was published on Tuesday in the journal Nature.

“We found large variation in predicted reopening risks: on average across metro areas, full-service restaurants, gyms, hotels, cafes, religious organizations, and limited-service restaurants produced the largest predicted increases in infections when reopened,” study authors wrote.

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Cell phone mobility data can accurately predict virus cases, researchers argued. (iStock)

Researchers argued that cell phone mobility data can predict virus cases with accuracy, and, based on the model, predicted that “a small minority of superspreader (events) account for a large majority of infections.” Further, they said limiting maximum occupancy at restaurants and other non-residential places is more effective than uniformly reducing mobility; in other words, officials should opt for a targeted approach.

Indeed, officials in many states have already pinpointed bars and full-service restaurants as major contributors to virus spread throughout the pandemic. However, even during newly imposed restrictions, sweeping closures or curfews across all non-essential businesses are usually the norm.

"Our results can guide policymakers seeking to assess competing approaches to reopening," study authors continued. "Despite growing concern about racial and socioeconomic disparities in infections and deaths, it has been difficult for policymakers to act on those concerns; they are currently operating without much evidence on the disparate impacts of reopening policies, prompting calls for research that both identifies the causes of observed disparities and suggests policy approaches to mitigate them."

The team also said the model accurately reflected disproportionate rates of virus spread among disadvantaged groups. “We find that disadvantaged groups have not been able to reduce mobility as sharply, and that the [non-residential areas] they visit are more crowded and therefore higher-risk,” study authors wrote.

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Americans' mobility dropped sharply in March, and researchers noted that Chicago in particular saw a 55% drop to non-residential areas. These “mobility reductions can dramatically reduce infections,” study authors wrote, finding that if Chicagoans only dropped mobility by 25%, predicted infections would have been over three times higher.

If a small number of these so-called “points of interest” or non-residential locations, lend most of the infections, then reopening strategies targeting these risky locations could be particularly effective, authors wrote. For example, “capping at 20% maximum occupancy in the Chicago metro area cut down predicted new infections by more than 80% but only lost 42% of overall visits.”

“These results support earlier findings that precise interventions, like reducing maximum occupancy, may be more effective than less targeted measures, while incurring substantially lower economic costs,” authors continued.

Also, by the simulation’s end, those earning the lowest income faced the highest chance of infection. More specifically, lower-earning groups of people were found to visit the grocery store more often, and stayed for longer, than higher-earning populations, thus increasing predicted infections.

There were limitations in the simulation, however, because it doesn’t capture the entire population, locations, and real-world factors associated with disease transmission.

Some suggested solutions to help disadvantaged populations avoid infection were tighter restrictions on allowed maximum occupancies, emergency food distribution centers to decrease the amount of people in high-risk stores, and free, available testing in high-risk virus neighborhoods, among other recommendations.

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