Diagnosing and containing a disease outbreak, or the health effects of a disruptive event like a natural disaster, can be a huge task. A study out Friday from New York University suggests that a new machine learning model could improve health officials’ ability to respond to future pandemics and other public health crises. The research was done in partnership with Carnegie Mellon University and New York City’s Department of Health and Mental Hygiene. Marketplace’s Kimberly Adams speaks with Daniel Neill, a computer science professor at NYU and the director of its Machine Learning for Good Laboratory, which released the study. He explains how this machine learning model works.