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In cities around the world, coronavirus outbreaks have been linked to restaurants, cafes and gyms. Now, a new model that uses cell phone data to map people’s movements suggests that these places could explain the majority of COVID-19 infections in US cities, according to an article published on Nature.com .
The model, published a few days ago in the journal Nature, also reveals how reducing occupancy can significantly reduce the number of infections.
The model “has concrete indications of effective measures to limit the spread of the disease, while limiting economic losses,” says Thiemo Fetzer, an economist at the University of Warwick in Coventry.
Mobility data
To predict how human movements might affect viral transmission, the research team fed anonymized location data from mobile apps into a simple epidemiological model that estimated how fast the disease spreads. The location data, collected by SafeGraph, a Denver, Colorado-based company, comes from 10 of the largest cities in the United States, including Chicago, Illinois; New York; and Philadelphia, Pennsylvania. He mapped how people commute to and from neighborhoods to points of interest such as restaurants, churches, gyms, hotels, car dealerships, and sporting goods stores for 2 months starting in March.
When the team compared the model’s number of infections in Chicago neighborhoods between March 8 and April 15 with the number of officially recorded infections in those neighborhoods a month later, they found that the model accurately predicted the number of cases. confirmed.
“We can accurately estimate the network of contacts between 100 million people for every hour of the day. This is the secret ingredient we have,” says Leskovec.
The most dangerous places and solutions to reduce the transmission of SARS-CoV-2
The team then used the model to simulate different scenarios, such as reopening some places and keeping others closed. They found that opening restaurants at full capacity led to the largest increase in infections, followed by gyms, cafes, and hotels / motels. If Chicago had reopened restaurants on May 1, there would have been nearly 600,000 additional infections that month, while the opening of gyms would have produced 149,000 additional infections. If all these businesses had been opened, the model predicts that there would be 3.3 million additional cases.
But limiting the occupancy of all these places (restaurants, cafes, gyms) to 30% would reduce the number of additional infections to 1.1 million, estimates the model. If employment were limited to 20%, new infections would drop by more than 80% to about 650,000 cases.
“The study highlights how big data on real-time population mobility offers the potential to predict transmission dynamics at unprecedented levels,” said Neil Ferguson, an epidemiologist at Imperial College London.
Why the poor are more prone to infections
Mobility data also suggests why people in poorer neighborhoods are more likely to be infected with SARS-CoV-2: because they have less ability to work from home, and the stores they visit for essentials are often more vulnerable. crowded than other areas. The average grocery store in the poorest neighborhoods had 59% more visitors per hour per square meter, and visitors stayed on average 17% longer than in stores outside those areas. Leskovec says that people who live in these areas likely have limited options for visiting less crowded stores, and as a result, a shopping trip is twice as risky as for someone in a wealthier area.
But Christopher Dye, an epidemiologist at the University of Oxford, says these mobility patterns need to be validated with real-world data. “It is an epidemiological hypothesis that remains to be tested. But it is a hypothesis that is worth testing,” he says.
Global trend
Generally speaking, Fetzer says, the modeling study corroborates much of what has been found in follow-up studies around the world, which have identified restaurants, gyms, choirs, nursing homes and other locations. crowded interiors like dangerous places where many people get infected at the same time.
Last month, Fetzer published a study showing how a British government program called “Eat Out to Help Out”, in which restaurant meals were subsidized in August, led to a large increase in visits to restaurants and accounted for 17% of new SARS-CoV-2 infections that month.
But restaurants may not be “hot spots” for COVID to spread everywhere. Tracing contacts in Germany found that restaurants were not the main source of infection in that country, says Moritz Kraemer, who models infectious diseases at the University of Oxford in the United Kingdom. This may be due to the fact that it can be difficult to identify the source of an infection using contact tracking data. Although the model’s prediction of global infection rates in cities has been validated with real-world data, Kraemer says more detailed contact tracking data will be needed to test whether the model correctly identified the actual location of infections.
Leskovec says that all models have a certain error. But because many of its predictions line up with observational data, he adds, there’s no reason to believe it wouldn’t work on a smaller scale. If the model is found to accurately predict the risk of visiting certain places, health officials could use it to adjust social distancing policies, Ferguson says.
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