Characteristic structure within multiple early warning signals
Determining a characteristic structure within multiple early warning signals via machine learning and statistical approaches.
- This project aims to use statistical analysis and machine learning to determine a characteristic structure within multiple early warning signals driven by a disease outbreak. More specifically, we will develop machine-learning models to detect the structural signatures in the coordinated surveillance data of EWS1 and to accurately identify the presence of an outbreak. We will then use statistical analyses and machine learning techniques to detect early warnings from objective EWS2. Bayesian networks will be applied to identify key features and reveal the statistical dependencies between the features.
- Co-Project Investigators: Mark Lewis, Hao Wang, Russ Greiner (University of Alberta) and Pouria Ramazi (Brock University)