The ability to forecast how an infectious disease like COVID-19 will behave is a critical tool for public health officials.

On this podcast, we sat down with Dr. Roni Rosenfeld, a computer scientist and a leader in the field of disease forecasting. Rosenfeld leads the machine learning department at Carnegie Mellon University in Pittsburgh and also works with Carnegie Mellon’s Delphi Research Group, which is one of several organizations that are part of the newly developed Outbreak Analytics and Disease Modeling Network established by the Centers for Disease Control and Prevention. 

Rosenfeld explained that, over more than a decade, researchers working on disease forecasting have taken weather forecasting as their model in creating usable tools to better understand the path of infectious diseases. He explained the type of data disease forecasters use – everything from hospital records to Google searches—to develop their forecasts and how that information can help those in health care. He also discussed why it’s important for legislators and others in state government to understand how to use and interpret disease forecasting.

Resources

• Machine Learning Department, Carnegie Mellon University