The Computational Epidemiology and Public Health Informatics Laboratory focuses on combining tools from Systems Science, Data Science, Computational Science and Applied Mathematics to inform decision making in health & health care and community safety & well-being.
CEPHIL has contributed dozens of published applications of agent-based, compartmental modelling and in diverse health & health care areas and guiding analytics that have informed important policy and investment decisions in the health system. The lab has further contributed techniques hybridizing multiple simulation approaches with machine learning tools and which leverage such hybrid models with data from multiple high-velocity data sources, innovations to improve dynamic modelling quality and efficiency, introduced novel modelling languages, and methods to enhance dynamic modelling formulation using category theoretic approaches.
Beyond the above, CEPHIL has contributed many data science contributions over the years, including contributing to the development of diverse epidemiological surveillance data collection systems, most prominently the Google Android-, iPhone- and web-based Ethica Data platform applied in hundreds of health studies around the world, with many using health-related sensors associated available on smartphones and wearable technologies.
An important sphere of CEPHIL’s work in the COVID-19 pandemic has lain in providing analytics to the health system. Through cross-leveraging combinations of dynamic modelling, Artificial Intelligence/Machine Learning, and diverse data sources, CEPHIL delivers COVID-19 situational analyses and short-term forecasts daily for the health system, with current or past long-term reporting being carried out for a combination of Canadian provincial and federal health system partners.