As people continue to dine out and go to the gym for their workouts, a recent study has indicated that those are among the places where many COVID-19 cases in the U.S. have emerged.
The findings, which were published this week in the journal, “Nature,” seem to confirm that most COVID-19 transmissions occur at places considered “superspreader” sites including gyms, full-service restaurants and cafes where people remain in close proximity to one another for a long time.
A team of Stanford University-led researchers created a computer model that predicted COVID-19′s spread in 10 major cities including Atlanta, New York, Chicago, Miami and Los Angeles. They did so by analyzing three factors that include the risk of infection: where people go throughout a given day, how long they stay and how many other people are visiting the same place simultaneously, according to a Tuesday news release.
“We built 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. Based on all of this, we could predict the likelihood of new infections occurring at any given place or time,” Jure Leskovec, the Stanford computer scientist who led the effort, said in a statement.
The study also involved researchers from Northwestern University.
To conduct the study, researchers followed the movements of 98 million Americans in 10 of the biggest metro areas in the country through 50 million different places including gyms, sit-down restaurants, new car dealerships and pet stores. Data was provided by SafeGraph, which provides point-of-interest data and foot traffic insights, and gave information on which of the 553,000 public locations people visited each day. It also showed how long people were there and the square footage of each establishment so that researchers could determine the hourly occupancy density.
Researchers used a model to produce and improve a set of equations to compute the probability of infectious events at different places and times. They provided data on how many COVID-19 infections were reported to health officials daily in each city. After getting transmission data for 10 cities, the team asked the model to multiply each city’s transmission against the mobility patterns in their database to determine new COVID-19 infections. The determinations closely followed real-life health official reports, which resulted in confidence in the reliability of the model.
“By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19,” researchers wrote in the abstract.
According to Yahoo Life, researchers found the “largest predicted increases in infections when reopened” were produced by full-service restaurants, gyms, hotels, cafes, religious organizations and limited-service restaurants, which include takeout spots and fast food places. More than 80% of infections were linked to such places.
Find out more about the study, including how infections were predicted, by reading the news release here.
