Using cellphone data from 1 in 3 Americans, researchers have identified the indoor public places most responsible for the spread of Covid-19 in the spring, and they argue that sharply limiting the occupancy of these locales — chiefly restaurants, gyms, cafes, hotels, and houses of worship — could control the raging pandemic without resorting to lockdowns.
Their analysis also explains how disparities in risk contribute to the disproportionate disease burden borne by people of color who have been less able than higher-income white people to work remotely and who tend to visit grocery stores and other places that tend to be smaller and more crowded than those in white neighborhoods.
“It corresponds to what we thought from the beginning, that there are certain activities that lead to spread more than other activities,” said Amesh Adalja, a senior scholar at the Johns Hopkins Center for Health Security who was not involved in the study. “When you are thinking of targeted public health interventions, it’s important to focus on those activities where that’s occurring and not being overly blunt and blocking and stopping all activities that may not necessarily have been a major contributor to spread.”
Mining mobile phone data, scientists tracked 98 million people’s hourly movements from March 1 to May 2 to places they visited regularly, and then mapped their movements to nearly 533,000 locations onto models of infectious disease spread. The simulated transmission rates accurately predicted actual daily case counts in neighborhoods of 10 large metropolitan areas, including Chicago, New York City, and San Francisco. That allowed them to identify which “superspreader” venues pose the greatest risk, which socioeconomic factors matter, and what works to diminish the danger.
“Our results suggest that infection disparities are not the unavoidable consequence of factors that are difficult to address in the short term, like differences in preexisting conditions,” Jure Leskovec of Stanford and co-authors wrote in their paper published Tuesday in Nature. “On the contrary, short-term policy decisions can substantially affect infection outcomes by altering the overall amount of mobility allowed and the types of [places] reopened.”
In Chicago, for example, 10% of the places people visited accounted for 85% of the predicted infections. Full-service restaurants, fitness centers, and places of worship had the highest overall risk for disease transmission, but that varied depending on the neighborhood. Across the 10 cities, snack bars and cafes were visited more often by people in high-income neighborhoods, but risk of disease transmission was higher in these businesses located in lower-income neighborhoods.
Cellphone records don’t reveal the race, ethnicity, or income of their users, but the scientists discovered that their models were still able to predict higher infection rates among racial and socioeconomic groups based on census data. A larger proportion of people who went to restaurants in low-income areas than high-income neighborhoods were infected, for example. People in lower-income neighborhoods bought their food at smaller, more crowded grocery stores with 59% more people per square foot compared to markets in wealthier neighborhoods where more white people lived. They also visited grocery stores more often and spent 17% more time shopping there.
The risk of Covid-19 infection climbed with the time spent in indoor public spaces. The study’s model predicts that setting an occupancy ceiling of 20% of maximum capacity for all these public spaces could cut new infections by more than 80% while reducing the overall number of visits by 42%.
Analyzing what happened in the spring limits its applicability to the fall, said Adalja, who is also an infectious disease physician. There are many more mitigation measures in place now in public places, from face coverings to temperature checks to occupancy limits. “If you went to a restaurant in early March, it’s a very different experience than going to a restaurant in early November.”
There are also limits to what mobility data can tell us now, he said.
“We’ve seen in the epidemiology that now it’s not restaurants or even large gatherings that are driving spread, but small gatherings,” he said, although the summer surge in Sun Belt states was partly driven by people crowding into bars. Heading into winter, spread has happened more in people’s homes than in public places. That means “people wouldn’t be captured by mobility data because they’re at home, right there in their neighborhood.”
Finding ways to help people stay home could help erase differences in people’s movements based on their income. In the Chicago metro area, overall visits to public places plummeted 54% in March, but in April people living in the lower-income neighborhoods were 27% more likely to travel to public places than people from higher-income neighborhoods. The authors said the gap probably reflects frontline workers holding jobs that could not be performed remotely.
Beyond capping occupancy at grocery stores, the scientists urge policymakers to open emergency food distribution centers. They also advocated for free, widely available testing in high-risk neighborhoods. To improve the lives of people who can’t work from home, they recommend better paid leave policy or income support so people can stay home when they are sick. For essential workers, they encourage better infection prevention in their workplaces, including high-quality PPE, good ventilation, and distancing when possible.
“These findings could have a valuable role in guiding policy decisions on how to reopen society safely and minimize the harm caused by movement restrictions,” Kevin Ma and Marc Lipsitch of the Harvard T. H. Chan School of Public Health wrote in a commentary published with the study.
Cases and deaths are already climbing, ahead of winter, when the virus can spread more easily in cold, dry weather that sends people indoors.
“I do think it’s going to be something where we have to get the entire population taking this very seriously, realizing that their behaviors have the most influence on the trajectory of these cases,” Adalja said.
In a teleconference with reporters, co-author Serina Yongchen Chang from Stanford said the researchers have been watching gross trends in Chicago showing that mobility is rising, but while infection rates did jump in the summer, they did not reach earlier pandemic levels from the spring.
“The behaviors that people are doing as they are moving around are having a meaningful impact on transmission: better social distancing, going to different places in different ways,” she said. “All of that is having an impact.”
The next step for the researchers is to make an interactive tool available to local officials navigating trade-offs between health and economic consequences when they consider reopening public places in their neighborhoods, Leskovec said on the call.
“Policymakers should be able to test different types of reopening based on the impact of the mobility on infections over time, as well as the disparities in infections across demographic groups,” he said.
I think you got it perfectly lmao. I can’t believe it just as much you can’t lol.
This doesn’t make sense. Illinois shut down eat in dining, schools, salons, etc in mid March and things didn’t start to open up until late May. Basically, the bulk of the data for this would have come from the first 3 weeks of March.
So let me make sure i have this straight. Non-white low income folks are getting infected at gyms, restaurants, snack-bars, grocery stores and bars. Really. Does the paper explain how someone on a “low-come” can afford to eat out at restaurants, afford alcohol thats marked up at bars, and afford gym memberships and stopping by local snack-bars when they are not working out or getting drunk? Whats really beautiful though is that the model can predict all this. Simply amazing. Of course it wouldn’t have thing to do with the fact that thats because a model’s ability to predict is based upon the data it is fed. If you feed a model data that says that non-whites commit more crimes and then ask the model to “predict” who is going to commit more crimes, guess, go ahead and guess what the model is going to predict. Science, you can’t beat it. Unless of course your a stupid little piece of RNA wrapped up in a protein overcoat and PhD’s don’t impress you at all. Its been over 100 years since the Spanish Flu, they didn’t even know what RNA was. Are we any better today at stopping this? We are sooooooo much smarter. Aren’t we?????
This article is offensive! Couldn’t you report this Covid update without your racist statements? Covid doesn’t have a color!
this is an infomercial if you follow the link
I think this is actually an infomercial if you follow the link
You are yet another confused person, writing about something totally irrelevant to the article’s topic. Just trying to get attention, right? Stop that nonsense, please – direct it to another more applicable venue.
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