Machine-learning techniques for predicting the evolution of an epidemic

Machine learning techniques can provide an assumption-free analysis of epidemic case data with surprisingly good prediction accuracy and the ability to dynamically incorporate the latest data, a new KAUST study has shown. The proof of concept developed by Yasminah Alali, a student in KAUST's 2021 Saudi Summer Internship (SSI) program, demonstrates a promising alternative approach to conventional parameter-driven mechanistic models that removes human bias and assumptions from analysis and shows the underlying story of the data.

source https://medicalxpress.com/news/2022-04-machine-learning-techniques-evolution-epidemic.html