Research summary
The COVID-19 pandemic wasn’t this century’s first infectious disease wave: we have also seen measles, Ebola and H1N1 outbreaks, to name a few. Scientists use various approaches to study outbreaks like these, including trait evolution models and stochastic compartmental models. Stochastic models can classify a population into groups by health status and estimate potential outcomes. But the lack of efficient inference methods and rigorous theory has hindered their application.
Dr. Lam Ho, Canada Research Chair in Stochastic Modelling, is developing software that can efficiently analyze real-world epidemic data using stochastic compartmental models. He and his team are also establishing a complete asymptotic theory for trait evolution models and using machine learning to build a framework for analyzing complex models. Their work will provide essential information for controlling epidemics by measuring the severity of ongoing outbreaks and predicting future risks.