Organizers:
Amy Hurford (Memorial University), Michael Li, Public Health Agency of Canada
Description:
Homogeneous mixing and the aggregation of diverse population groups into one group are frequent simplifying assumptions that may produce erroneous models and recommendations that exacerbate health inequities. Yet, models that make these simplifying assumptions have well-understood dynamics, and can be quickly solved, facilitating data fitting and uncertainty analysis that can support policy recommendations. Advancing the methodology around these model-building tensions is needed, and the best modelling approach may depend on the application. The motivation for developing these modelling approaches is from the COVID-19 pandemic in Canada. Atlantic Canada, the Canadian territories, and other small Canadian jurisdictions experienced different epidemiology, and needed different types of modelling support, than the larger Canadian provinces. There is a need to advance infectious disease modelling to support jurisdictions at all levels, and this session furthers this goal by including talks that describe: infectious disease spread in structured communities; importations and mobility networks; models that were developed for specific small jurisdictions, methods for calculating the reproduction number, estimating healthcare demand, and describing how the needs of small jurisdictions can be integrated into pandemic preparedness plans.
Wade McDonald
University of Saskatchewan"Use of Synthetic Data to Improve Wastewater-based Epidemiological Models in a Small Jurisdiction"
Matthew Betti
Mount Allison University"Modeling healthcare demand during a disease outbreak"
Sicheng Zhao
McMaster University"Edge-based Modeling for Disease Transmission on Random Graphs – an Application to Mitigate a Syphilis Outbreak"
Caroline Mburu
British Columbia Centre for Disease Control/Simon Fraser University"Wastewater-based modelling for Mpox surveillance among gbMSM in BC"
