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)
Immediately related elementsHow this works
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EIDM  Â»EIDM 
Networks Â»Networks
OMNI Â»OMNI
OMNI – Research Â»OMNI – Research
03 Early Warning Systems of Infectious Diseases Â»03 Early Warning Systems of Infectious Diseases
Characteristic structure within multiple early warning signals
Mark Lewis Â»Mark Lewis
Hao Wang Â»Hao Wang
Russell Greiner Â»Russell Greiner
Pouria Ramazi Â»Pouria Ramazi
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