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The COVID-19 pandemic is rising once again to unprecedented levels in the US, and all signs point to a bleak winter marked by the return of many curfews and closings. The conversation now turns to what part of the economy can continue to function without irresponsibly allowing the transmission of COVID-19 during a deadly surge. To that end, a team of researchers led by Stanford University has created a model that uses cell phone mobility data to map the most dangerous hubs for COVID-19 transmission in a given city.
The study used a combination of demographic data, epidemiological data, and anonymous cell phone location data to track how nearly 100 million Americans moved from March to May in ten major cities: Atlanta, Chicago, Dallas, Houston, Los Angeles, Miami. , New York, Philadelphia, San Francisco and Washington. The location data was provided by SafeGraph, which offered mobility data combined with more than 550,000 public places such as shops, restaurants and churches, and showed how many people visited those establishments, when they visited them and for how long. The dataset even included the square footage of those locations, which allowed for density calculations.
The researchers then began fitting a model to predict infections in an area, playing with the variables until the “predicted” results of past infections matched the actual reported infections. Once the predicted and reported infections matched, they tested the model on future infections, and the model closely followed reality. Finally, the researchers integrated demographic data to understand how different populations were affected.
“We created a computer model to analyze how people of different demographic backgrounds and from different neighborhoods visit different types of places that are more or less crowded,” said Jure Leskovec, the Stanford computer scientist who led the research, in an interview with Tom Abate. Stanford. “Based on all of this, we could predict the likelihood of new infections occurring at any time or place.”
The model identified a number of common “super-spread” sites in any city, with restaurants, gyms, and cafes proven to be common culprits due to prolonged exposure to strangers in a relatively high-density environment.
Additionally, however, the integrated model painted a picture of how the ubiquitous pandemic was far from being equally distributed. The model showed how minority and low-income residents were disproportionately exposed to these dangerous conditions, a new factor compounding earlier assumptions about higher infection rates among those populations and opening the door to new solutions.
“In the past, these disparities were supposed to be driven by pre-existing conditions and unequal access to healthcare, while our model suggests that mobility patterns also help drive these disproportionate risks,” said David Grusky, professor of sociology. at Stanford. “Because places that employ minorities and low-income people tend to be smaller and more crowded, occupancy limits on reopened stores can reduce the risks they face. We have a responsibility to develop reopening plans that eliminate, or at least reduce, the disparities that current practices are creating. “
The opposite was true for populations with socioeconomic advantages: in areas where more people were able to stay at home, infection rates were lower. Overall, Leskovec said, the model offered “the strongest evidence yet” of the effectiveness of stay-at-home policies in curbing infections.
The model and its data are now publicly available.
