Texas A&M predicting COVID-19 spread with deep-learning model

Researchers, who said their tool has a 64% accuracy rate, are learning which factors are most predictive of the virus' spread.
brain on circuit board with words "deep learning'
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Researchers at Texas A&M University have recently begun using artificial intelligence to forecast the growth of COVID-19 cases in communities across the country.

The university is using a deep-learning model, a method of machine-learning that relies on large amounts of data, to process data related to population activities and mobility to help predict the spread of COVID-19 at a county level. Ali Mostafavi, the project’s lead researcher, said this work could help lawmakers make informed policy decisions to protect residents and mitigate spread of the virus.

“Significant opportunities exist using these big data and AI to contain the existing pandemic and also better prepare and mitigate the future pandemics,” said Mostafavi, an associate professor of civil and environmental engineering.

According to an announcement from the university last week, data fed into the model included the movement of people within communities, census data, social-distancing data, past case count growth and social demographics. The model predicted growth of future COVID-19 cases with 64% accuracy, Mostafavi said. The model’s predictions were most accurate when looking seven days ahead, with accuracy decreasing further into the future.


But being able to predict the growth of future cases is not the only important outcome of developing the model. Mostafavi said it also helps identify which factors — like population mobility or social demographics — affect the spread of the virus most, which could inform public policies, like stay-at-home orders.

“This model does not identify specific mitigation and response strategies, but it can help at different points in time to see which strategies could be effective based on various county-level features,” Mostafavi said in a university press release.

At the beginning of the pandemic, the researchers found travel-related and mobility-related factors to be important predictors of case growth, but later found other factors, such as travel to points of interests and social demographics, have become more important.

In the future, Mostafavi said he and other researchers will use new data sets to develop different types of models, including one for city-scale surveillance to predict cases at the ZIP-code level.

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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