Mathematical modelling for pandemic preparedness in Canada: Learning from COVID-19

Area of Science:

• Public Health
• Epidemiology
• Mathematical Modeling

Background:

• The COVID-19 pandemic highlighted the importance of pandemic planning and the role of mathematical modeling in informing public health decisions.
• Various modeling approaches, including compartment, agent-based, and importation models, are essential for pandemic preparedness.
• Best practices emphasize biological realism, multidisciplinary collaboration, appropriate model complexity, uncertainty assessment, and clear communication.

Purpose of the Study:

• To review the application of mathematical modeling in infectious disease control based on COVID-19 experiences.
• To outline how mathematical modeling can best support pandemic preparedness in Canada.
• To identify key challenges and recommendations for enhancing modeling capabilities within public health.

Main Methods:

• A narrative review was conducted, drawing on the collective experiences of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID).
• The review synthesized insights from a national community of practice focused on mathematical modeling for public health.

Main Results:

• Mathematical modeling supports pandemic preparedness by estimating resource needs for hypothetical pandemics and enabling rapid adaptation for forecasting emerging threats.
• Modeling can inform decisions on interventions and long-range epidemic projections.
• Two primary applications identified: resource needs estimation and adaptable forecasting systems.

Conclusions:

• Enhancing modeling expertise within Canadian public health organizations is critical.
• Fostering a community of practice linking public health and academic modelers is vital for effective emergency response.
• Key challenges include ensuring access to linked public health, hospital, and genomic data.