Can machine learning help predict disease spread?

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. Working with KAUST's Ying Sun and Fouzi Harrou, Alali leveraged her experience working with artificial intelligence models to develop a framework to fit the characteristics and time-evolving nature of epidemic data using publicly reported COVID-19 incidence and recovery data from India and Brazil. "My major at college was artificial intelligence, and I previously worked on a medical project using various ML algorithms," says Alali. "Working with Professor Sun and Dr Harrou during my internship, we considered whether the Gaussian Process Regression method would be useful for predicting pandemic spread because it gives confidence intervals for the predictions, which can greatly assist decision-makers." Accurate forecasting of cases during a pandemic is essential to help mitigate and slow transmission.