With so many eyes fixed on New York City as the “epicenter” of the COVID-19 crisis in the U.S., it might go unnoticed at the national level that nearly 60,000 infections could be recorded some 150 miles to the north by June 8.
That’s according to a professor of computer science at Rensselaer Polytechnic Institute (RPI) in Troy, N.Y., who has developed a predictive model for use by planners in the Empire State’s smaller cities.
Malik Magdon-Ismail, PhD, who has expertise in machine learning, data mining and pattern recognition, built his AI-aided model specifically for New York State’s Capital Region. The model incorporates data from Albany, Rensselaer, Saratoga, and Schenectady counties.
However, he tells RPI’s news division, building similar predictive tools for other small cities would be as easy as “running the numbers.”
The estimate of nearly 60,000 cases is based on 50% of residents in the region hewing to Gov. Andrew Cuomo’s stay-at-home order.
Bump the compliance rate to 75%, and the infection count won’t get above 30,000, according to Magdon-Ismail’s model.
Modeling smaller cities with machine learning “is a challenge in that few data points are available and updated less frequently than the picture of the nation as a whole or an epicenter like New York City,” reports RPI communications specialist Mary Martialay. “Generic machine learning operating on such data would likely produce inaccurate predictions. To compensate, Magdon-Ismail focuses on simple models and uses ‘robust’ algorithms that incorporate solutions beyond that of the mathematical ideal.”
To this Magdon-Ismail adds that the robustness comes from considering a collection of models “that have near-optimal levels of consistency with the data. I find a variety of models that fit the data, and then I use all of those models together to predict.”
To read the full news report from RPI, click here.