Internet Search Data Can Help Predict a Looming ‘Twindemic’

Ph.D student Simin Ma sits with Assistant Professor Shihao Yang at his desk. A computer monitor shows flu data and Google search trends that they used in their Covid and flu forecasting models.

Ph.D. student Simin Ma, left, and Shihao Yang, assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering. They developed a model that uses search data to predict coming waves of serious Covid-19 and flu cases that could burden healthcare resources. (Photo: Candler Hobbs)

The most widely used source of medical advice in modern society might be the Google search box.

Enough people turn to the site with searches like “loss of taste” or “how long contagious” that researchers at Georgia Tech can use that data to accurately predict looming waves of influenza-like illness and Covid-19 infections. Their forecasting models work for the nation overall and for each state, offering a new source of data about potential “twindemics” that could burden healthcare systems.

The model, developed by Shihao Yang and his team in the H. Milton Stewart School of Industrial and Systems Engineering, is published in the Nature journal Communications Medicine.

Read the full story on the College of Engineering website.

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Joshua Stewart
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