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Researchers have developed a computer model that predicts how the new coronavirus is spreading in American cities.
The model examines three factors that play a role in the risk of infection: where people go during the day, how long they stay, and how many other people are visiting the same place at the same time.
He revealed that most cases of the virus occur at “ very widespread ” sites, such as coffee shops, full-service restaurants and gyms, where people are close to each other for long periods of time.
However, limiting the capacity in these places could reduce the number of infections by up to 75 percent.
The team, at Stanford University School of Engineering, says their model can be used as a tool for managers to determine the trade-off between new infections and reopening businesses, even at limited capacity.
A computer model from Stanford University found that most COVID-19 infections occur at “ very widespread ” sites, such as cafes, full-service restaurants and gyms. Pictured: A medical worker wears a face mask at a pizzeria in New York City, November 9
In New York City, the model found that full reopening would result in more than six million infections, but a 20% occupancy would reduce the number of infections by 80% (left). In Chicago, 100% full occupancy would result in almost 3.5 million cases, but a 20% occupancy cap would only lead to around 500,000 new infections (right)
In Los Angeles, full capacity would lead to more than three million new cases, but limiting occupancy would reduce the number of infections by 75% (above)
“We built a computer model to analyze how people from different demographics and from different neighborhoods visit different types of more or less crowded places,” said Dr Jure Leskovec, associate professor of computer science at Stanford University.
“Based on all of this, we could predict the likelihood of new infections at some place or time.
For the study, published in the journal Nature, the team followed the movements of people in the 10 largest cities: Atlanta, Chicago, Dallas, Houston, Miami, Los Angeles, New York, Philadelphia, San Francisco, and Washington. DC.
The cell phone data tracked 98 million Americans who visited 553,000 public places, including restaurants, gyms and religious buildings between March 8 and May 9.
He looked at the establishments people visited each day, how long they did so, and the square footage to determine hourly occupancy density.
In real life, researchers cannot predict when and where a contagious person may come in contact with a susceptible person and spread the disease.
But using the model, the team determined the rate of transmission of the virus in each city by calculating the probability of infectious events at different places and times.
To make sure the forecast was accurate, it was compared with the number of COVID-19 infections reported to health officials in each city each day.
For each city, the researchers then created a COVID-19 infection / activity tradeoff graph.
In Chicago, for example, a full reopening to 100% full occupancy would result in nearly 3.5 million infections in a month.
However, a A 20% occupancy cap would only lead to about 500,000 new infections – risking only 18% of the infections that would occur if public places fully reopened.
In New York, full reopening would lead to more than six million infections within one month of reopening.
But a 20 percent occupation would reduce the number of infections by 80 percent.
And, in Los Angeles, opening sites to full capacity would result in more than three million new infections the following month, but limiting capacity would reduce the number of infections by 75%.
The researchers say the model shows how reopening businesses with lower occupancy limits could help minority communities the most.
“ Because places that employ minority and low-income people are often smaller and more crowded, occupancy caps for reopened stores can reduce the risks they face, ” the co- author, Dr. David Grusky, professor of sociology at Stanford’s School of Humanities Sciences.
“We have a responsibility to develop reopening plans that eliminate – or at least reduce – the disparities that current practices create.
The team is currently working to develop the model into a user-friendly tool for use by policy makers and public health officials.
“In principle, anyone can use this model to understand the consequences of different policy decisions about staying at home and closing a business,” Leskovec said.
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