Computer model uses cellphone data to predict COVID-19’s spread in US cities

A team of researchers from Stanford and Northwestern universities have created a computer model that predicts how COVID-19 spreads in cities by studying anonymized cellphone data from 98 million Americans in 10 major cities.



a group of people standing in front of a crowd: A crowd of people gathers to drink at a bar in Columbs, Ohio, May 15, 2020.


© Columbus Dispatch via USA Today Network, FILE
A crowd of people gathers to drink at a bar in Columbs, Ohio, May 15, 2020.

Their findings indicate that most infections occur at so-called super-spreader sites where people are in contact for long periods of time, such as at restaurants or gyms that have reopened at or near full capacity. Researchers said the model also can help explain some of the reasons COVID-19 has hit communities of color particularly hard.


MORE: Tents, igloos and barriers, here’s how the virus can spread from table-to-table

The new research comes at a time when new COVID-19 cases in the U.S. are surging across the country and cities have struggled

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Research team creates a computer model that can predict how COVID-19 spreads in cities

Stanford-led team creates a computer model that can predict how COVID-19 spreads in cities
A new computer model predicts the COVID-19 infection-versus-activity trade-off for Chicago. According to the figure, COVID-19 infections will rise as the number of visits to businesses and public places approach pre-pandemic levels. However, restricting maximum occupancy can strike an effective balance: for example, a 20 percent occupancy cap would still permit 60 percent of pre-pandemic visits while risking only 18 percent of the infections that would occur if public places were to fully reopen. Credit: Serina Yongchen Chang

A team of researchers has created a computer model that accurately predicted the spread of COVID-19 in 10 major cities this spring by analyzing three factors that drive infection risk: where people go in the course of a day, how long they linger and how many other people are visiting the same place at the same time.

“We built a computer model to analyze how people of different demographic backgrounds, and from

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Computer model can predict COVID-19’s spread

A team of researchers has created a computer model that accurately predicted the spread of COVID-19 in 10 major cities this spring by analyzing three factors that drive infection risk: where people go in the course of a day, how long they linger and how many other people are visiting the same place at the same time.

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Video by Kurt Hickman

A study of how 98 million Americans move around each day suggests that most infections occur at “superspreader” sites that put people in contact for long periods, and details how mobility patterns help drive higher infection rates among minority and low-income populations.

“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

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Computer model can predict when tornadoes hit the UK

Forecasters can predict when a tornado is likely to hit in the UK with more accuracy thanks to a new computer model.

Around 30 tornadoes occur in the UK each year, 40% of which develop on cold fronts – which specifically occur on the boundary between two masses of air where cold air is advancing forward.

A lack of forecasting methods for these conditions means these tornadoes often strike without warning .

Researchers at the University of Leeds and the Met Office have for the first time created a prediction for how likely tornadoes are to occur on cold fronts.

They say this means a more accurate assessment of tornado risk can be made before a cold front crosses the UK.

Matthew Clark, a Met Office scientist who is currently studying for a PhD at Leeds’s school of earth and environment, said: “Tornadoes are a relatively common weather hazard on

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