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Officials considering reopening plans (or lockdowns) amid the coronavirus could look to mobile phone mobility data to better inform decisions, a new study suggests.
Researchers at Stanford University, among other institutions, studied anonymized data on 98 million people and their hour-by-hour movement patterns in the 10 largest metropolitan areas in the U.S. An early version of the findings assessed by des peers was published Tuesday in the journal Nature.
“We have seen wide variation in the expected reopening risks: On average, in metropolitan areas, full-service restaurants, gyms, hotels, cafes, religious organizations and limited-service restaurants produced the increases. the most significant infections expected when reopening, ”wrote the study authors.
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Mobile phone mobility data can accurately predict virus cases, the researchers argued. (iStock)
The researchers argued that cell phone mobility data can accurately predict virus cases and, based on the model, predicted that “a small minority of super-broadcasters (events) make up a large majority of infections ”. In addition, they stated that limiting the maximum occupancy of 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 identified full-service bars and restaurants as the main contributors to the spread of the virus throughout the pandemic. However, even during the newly imposed restrictions, sweeping closures or curfews at all non-essential businesses are generally the norm.
“Our findings may guide policymakers seeking to evaluate competing approaches to reopening,” the study authors continued. “Despite growing concerns about racial and socio-economic disparities in infections and deaths, it has been difficult for policy makers to respond to these concerns; they currently operate without much evidence on the disparate effects of reopening policies, which has prompted calls for research that both identifies the causes. disparities observed and suggests policy approaches to reduce them. “
The team also said the model accurately reflects the disproportionate rates of spread of the virus among disadvantaged groups. “We find that disadvantaged groups have not been able to reduce mobility as strongly, and that the [non-residential areas] they visit are more crowded and therefore more at risk, ”wrote the study authors.
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Mobility for Americans fell sharply in March, and researchers noted that Chicago in particular had seen a 55% drop to non-residential areas. These “reductions in mobility can dramatically reduce infections,” the study authors wrote, finding that if Chicagoans had only decreased mobility by 25%, predicted infections would have been more than three times higher.
If a small number of these so-called “points of interest” or non-residential locations lend themselves the most infections, then reopening strategies targeting these risky locations could be particularly effective, the authors wrote. For example, “the cap at 20% maximum occupancy in the Chicago metro area reduced new infections by over 80%, but lost only 42% of all visits.”
“These results support previous findings that specific interventions, such as reducing maximum occupancy, may be more effective than less targeted measures, while resulting in significantly lower economic costs,” the authors continued.
In addition, at the end of the simulation, people with the lowest incomes were the most likely to be infected. Specifically, it was found that low-income groups of people visited the grocery store more often and stayed there longer than high-income populations, thus increasing predicted infections.
However, the simulation had limitations, as it did not capture the entire population, locations, and real-world factors associated with disease transmission.
Some suggested solutions to help underprivileged populations avoid infection were tighter restrictions on maximum occupancies allowed, emergency food distribution centers to reduce the number of people in high-risk stores, and free and available testing. in neighborhoods with high risk viruses, among other recommendations.
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