Following the COVID-19 pandemic, the Public Health Agency of Canada (PHAC) is updating Canada’s Pandemic Influenza Plan into a broader Canadian Pandemic Preparedness Plan (CPPP), creating a need for modelling frameworks that support forecasting and decision-making across diverse and uncertain pathogen characteristics. To address this, the PHAC Modelling Hub developed a dual-model framework that enables rapid scenario exploration without precluding more detailed analyses.

We paired a deterministic, age-structured compartmental model with a stochastic, agent-based model to simulate outbreaks of five “hypothetical-yet-plausible” respiratory diseases based on profiles of real respiratory pathogens of pandemic potential. Using Canadian demographic and contact data with literature-derived epidemiological parameters, we generated forecasts under baseline conditions and intervention scenarios, focusing primarily on the first 100 days of an outbreak, but also exploring longer-term outcomes.

Disease transmissibility drove the timing and magnitude of epidemic peaks, while virulence determined healthcare burden. The agent-based model produced spatially heterogeneous, often multimodal epidemic trajectories, revealing uncertainty not captured by more homogeneous models. The compartmental model identified critical timing–effort trade-offs, with early, moderate contact reductions substantially reducing hospitalizations; agent-based simulations supported these findings while highlighting variability linked to population structure.

These results demonstrate how complementary models can improve early outbreak forecasting, quantify uncertainty, and identify time-sensitive intervention opportunities to support pandemic preparedness objectives.

Keywords: time series forecasts; pandemic preparedness; decision-making; public health