Lam Ho (Dalhousie University)
Epidemic Forecasting using Delayed Time Embedding.
Abstract:
Forecasting the future trajectory of an outbreak plays a crucial role in the mission of managing emerging infectious disease epidemics. Compartmental models, such as the Susceptible-Exposed-Infectious-Recovered (SEIR), are the most popular tools for this task. They have been used extensively to combat many infectious disease outbreaks including the current COVID-19 pandemic. One downside of these models is that they assume that the dynamics of an epidemic follow a pre-defined dynamical system which may not capture the true trajectories of an outbreak. Consequently, the users need to make several modifications throughout an epidemic to ensure their models fit well with the data. However, there is no guarantee that these modifications can also help increase the precision of forecasting. In this talk, I will introduce a new method for predicting epidemics that does not make any assumption on the underlying dynamical system. Our method combines sparse random feature expansion and delay embedding to learn the trajectory of an epidemic.
Speaker
Dr. Lam Ho is an Associate Professor, Canada Research Chair in Stochastic Modelling in the Department of Mathematics and Statistics at Dalhousie University. His research focuses on stochastic modelling, mathematical biology, machine Learning, evolutionary Biology, infectious disease epidemiology statistics, reliability and statistical computing.