New Hybrid Machine Learning Framework Extends Range of Accurate Epidemic Forecasting

Community leaders and public health officials may soon have more time to plan for Covid and flu outbreaks thanks to a new machine learning (ML) framework that is improving the accuracy of long-range epidemic forecasting. That is exactly what researchers at Georgia Tech’s School of Computational Science and Engineering (CSE) have developed through EINNs, Epidemiologically-Informed Neural Networks.