Early COVID-19 Pandemic Preparedness: Informing Public Health Interventions and Hospital Capacity Planning Through...

Area of Science:

• Public Health
• Health Systems Modeling
• Epidemiology

Background:

• Early COVID-19 pandemic required rapid policy decisions for public health measures and hospital capacity.
• Participatory modeling engaged stakeholders to support evidence-based decision-making.
• Simulation modeling is crucial for planning and mitigating disease outbreaks.

Purpose of the Study:

• To use simulation modeling to guide public health measures and hospital capacity planning for COVID-19 in Saskatchewan.
• To assess the impact of various mitigation strategies on disease transmission and healthcare burden.
• To inform policy-making through a participatory approach.

Main Methods:

• Developed a hybrid simulation model combining System Dynamics (SD), discrete-event simulation (DES), and agent-based modeling (ABM).
• SD modeled population-level transmission, ABM simulated individual progression and contact tracing, and DES captured patient flow.
• Examined the impact of physical distancing, testing, and contact tracing strategies.

Main Results:

• Enhanced contact tracing with mass testing significantly reduced COVID-19 transmission and mortality.
• Modeling indicated a substantial decrease in peak hospital and intensive care unit bed demand.
• The hybrid model effectively predicted the impact of public health interventions.

Conclusions:

• Participatory modeling effectively informed policy on contact tracing and hospital surge capacity.
• The hybrid simulation approach offers a novel method for pandemic response planning.
• Validation of provincial interventions through modeling enhanced confidence in adopted strategies.