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University of Texas built state’s COVID-19 dashboard

Using hospitalization and mobility data, the dashboard makes four-week predictions of the impact and spread of COVID-19.
Dashboard
(Getty Images)

To predict how COVID-19 will spread though Texas communities, researchers at the University of Texas, Austin developed a new COVID-19 data dashboard that can make four-week projections on the potential spread and impact of the virus, giving local leaders time to implement health guidelines before outbreaks get out of hand.

The dashboard, launched last week, tracks hospitalization data and anonymous cellphone mobility data to predict how the pandemic is progressing across the 22 Trauma Service Areas the state has been divided into to help citizens, school boards, city and county leaders, and health care providers make informed decisions for their families and communities, Lauren Meyers, director of the UT COVID-19 modeling consortium and a professor of integrative biology, told EdScoop.

The collected data tells the dashboard’s algorithm how much mobility there is within a community to predict how infection could spread between people, Meyers said.

“We know on a very local scale how much are people staying at home and how much are they out and about, and what kinds of places are they visiting,” she said. “And we’ve learned by looking at past data that there’s a direct link between those mobility patterns and the rate of spread of the virus in the community.”

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This data, coupled with the current number of cases within a community and hospitalization data allows the algorithm to make projections about how the virus will likely spread and how big its impact on a community will be, Meyers said.

To improve prediction accuracy, she said, the algorithm also considers data like the virus’ incubation period, how long recovery takes, how infectious people are on each day, the general age of people in the community and whether or not they have high risk conditions. Ultimately, Meyers said, detailed data and the predictions they generate can help communities across Texas prevent widespread infections like those seen at the beginning of the pandemic and surges that hit over the summer when local restrictions were lifted.

She said the dashboard might indicate to a community it’s doing a good job and should continue with its current measures, or it could alert local leaders and residents that they need to make swift changes to keep infections under control. And if the dashboard does predict a spike in COVID-19 cases, “the projections will help communities, hospitals, policymakers know how much time they have until their ICU and their hospitalization might come close to capacity,” Meyers said.

Dashboards have been incredibly useful during the pandemic for community leaders and residents to have a clear understanding of trends and the current threat level in their neighborhoods, she said, and being able to forecast the impact of COVID-19 is critical for keeping people safe and healthy.

“The real take home is that it took a lot of work and it took a lot of people, cooperation and good behavior to bring us to the point where we are now,” Meyers said, adding that to continue on a trend of getting COVID-19 under control, paying attention to data and making informed decisions based off of that data will be key.

Betsy Foresman

Written by Betsy Foresman

Betsy Foresman was an education reporter for EdScoop from 2018 through early 2021, where she wrote about the virtues and challenges of innovative technology solutions used in higher education and K-12 spaces. Foresman also covered local government IT for StateScoop, on occasion. Foresman graduated from Texas Christian University in 2018 — go Frogs! — with a BA in journalism and psychology. During her senior year, she worked as an intern at the Center for Strategic and International Studies in Washington, D.C., and moved back to the capital after completing her degree because, like Shrek, she feels most at home in the swamp. Foresman previously worked at Scoop News Group as an editorial fellow.

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