University of Michigan issued the following announcement on Aug. 25.
For Sabrina Corsetti, the pandemic presents an interesting problem—a data problem, that is. Her efforts to model the pandemic’s spread using a machine learning algorithm has now been included in those being aggregated for the CDC’s weekly projections.
orsetti, a senior majoring in physics and mathematics, had her previous research halted when the University of Michigan suspended in-person classes and labs back in March. Thomas Schwarz, one of Corsetti’s research professors, happened to be modeling the pandemic’s data and included her in the project.
Under Corsetti’s direction, the small analysis has developed into a full, data-driven research project. Corsetti says that, while she was originally interested in the pandemic due to the news, it was the amount of “unknowns” surrounding the data that inspired her to dive deeper.
“At the beginning, we didn’t know the scope or end goal, but we realized that the simple epidemiological models weren’t carrying us like we needed,” she said. “But then I came across a paper about applying machine learning to epidemiology, and I worked off of that to build better predictions based on the data alone and without any external assumptions.”
Corsetti’s new model performs ridge regression, which is a type of machine learning algorithm that finds a best-fit projection of future COVID-19 cases and deaths for the United States. The model is currently able to project up to 40 days in advance, using a method that centers a spectrum of predictions around a single optimal projection.
Original source can be found here.
Source: University of Michigan
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