Ishanu Chattopadhyay, PhD, a professor of medicine at the University of Chicago, and his postdoctoral scholar Yi Huang, PhD, drew on their previous experience modeling epidemics and expertise in machine learning to analyze years of past influenza epidemics and develop a new model for COVID-19. The new risk measure they developed — denoted as the Universal Influenza-like Transmission (UnIT) score — has proven to be better at predicting weekly case count forecasts than the best models currently described. The work was published October 14 in PLoS Computational Biology.

The researchers used 10 years of data on influenza hospitalizations nationwide to examine week-to-week trends in patients with the flu, allowing them to determine where infection clusters began and how they spread across the country each year. Using this data, they were able to produce the UnIT score. Combined with other variables known to be important in the spread of diseases like COVID-19, such as demographic details within a community, the model produced forecasting results that were more accurate on average than any of the other models listed on the CDC modeling hub.

Read more about the research at The Forefront.

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