CALMS: Modelling the long-term health and economic impact of Covid-19 using agent-based simulation

Area of Science:

• Epidemiology
• Health Economics
• Computational Modeling

Background:

• COVID-19 poses significant long-term health and economic challenges.
• Predictive modeling is crucial for understanding and mitigating pandemic impacts.

Purpose of the Study:

• To introduce the CoronAvirus Lifelong Modelling and Simulation (CALMS) model.
• To assess the lifelong health and economic impacts of COVID-19.
• To evaluate the effectiveness of intervention strategies.

Main Methods:

• Agent-based modeling simulating individual characteristics and comorbidities.
• Retrospective validation against UK hospitalization, ICU, and fatality data (March-September 2020).
• Prospective simulation of vaccination and lockdown strategies against a baseline.

Main Results:

• Periodic lockdowns yielded 6-8% reduction in adverse health outcomes at a high cost.
• Targeted vaccination programs achieved 89-90% reduction in adverse health outcomes at a lower cost.
• Vaccination targeting the clinically vulnerable provided substantial population-level healthcare protection.

Conclusions:

• Periodic lockdowns alone are insufficient for long-term COVID-19 health impact reduction.
• Targeted vaccination strategies are highly effective and cost-efficient for population health protection.
• The CALMS model provides a valuable tool for pandemic preparedness and intervention planning.