Speaker: Dr. Nathaniel Osgood, University of Saskatchewan

Abstract: While COVID-19 transmission models have conferred great value in informing public health understanding, planning and response, the ability of public health decision makers to rely purely upon traditional transmission models with pre-set assumptions -- no matter how favourably evidenced when built -- is challenged by numerous factors. The ongoing replanning associated with rolling back and re-instituting measures can strongly benefit from approaches that continuously integrate into rigorous transmission models so that shifts in epidemiology, behaviour, and availability of acute care resources, can be monitored in real time, and scenario-based projections can be made. We describe here the design, implementation and day-to-day use for Saskatchewan public health and clinical support decision making of a particle filtered COVID-19 compartmental model, using a Sequential Monte Carlo algorithm of Particle Filtering that is informed by different data sources. Model outputs include estimates of Rt, and counts of undiagnosed/diagnosed infectives.

Dr. Nathaniel D. Osgood (PhD MIT) is a Professor of Computer Science and Associate Faculty in Community Health & Epidemiology at USaskatchewan. His research is focused on providing cross-linked simulation, ubiquitous sensing, and machine learning tools to inform understanding of population health trends and health policy tradeoffs. His applications work has addressed challenges in the communicable, zoonotic, environmental, and chronic disease areas. Dr. Osgood is a co-creator of two novel mobile sensor-based epidemiological monitoring systems, most recently the Google Android- and iPhone-based iEpi (now Ethica Health) mobile epidemiological monitoring systems.