Candidate
Ali Haghighatgooasiabar | Applied Mathematics, University of Waterloo
Title
Deep Learning for Early Warning Signals of Complex Dynamical Transitions in Epidemics
Abstract
The identification of tipping points, which are frequently brought about by imperceptible alterations in internal structures or external disturbances, is crucial for forecasting dynamical systems, SUCH AS VACCINE-PREVENTABLE INFECTIOUS DISEASES. Based on empirical data, when either the vaccination rate or birth rate changes, the disease infection prevalence encounters a tipping point. This tipping point causes a transition between different limit cycles such as annual to biennial or more complex cycles. Identifying this transition helps control the epidemic. In this work, our aim is to design deep learning models to construct an early warning system to PROVIDE ADVANCE WARNING of these tipping points.