COMOKIT v2: A multi-scale approach to modeling and simulating epidemic control policies

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The COVID-19 pandemic highlighted the critical need for robust epidemiological models to predict disease spread and evaluate intervention strategies.
• Existing models often lack the flexibility to address questions across diverse geographical scales, limiting their practical application.
• Assessing the impact of interventions like containment policies and ventilation requires multi-scale modeling capabilities.

Purpose of the Study:

• To introduce the latest version of the COMOKIT toolbox, a suite of integrated epidemiological models.
• To demonstrate the toolbox's capability to analyze public health policies at various geographical scales, from micro (building) to macro (country).
• To provide a framework for understanding and managing epidemics through multi-scale simulation.

Main Methods:

• Integration of three distinct models: COMOKIT-micro, COMOKIT-meso, and COMOKIT-macro.
• Development of a unified toolbox enabling analysis across different spatial resolutions.
• Application of the integrated models to simulate and evaluate various COVID-19 public health interventions.

Main Results:

• The COMOKIT toolbox successfully integrates micro, meso, and macro models for comprehensive epidemic analysis.
• The tool facilitates the assessment of intervention policies at granular (building) and broad (national) scales.
• Demonstrated utility in addressing diverse public health questions related to COVID-19 transmission and control.

Conclusions:

• The integrated COMOKIT toolbox offers a scalable and versatile solution for epidemiological modeling.
• It enhances the ability to predict epidemic trajectories and assess the effectiveness of public health interventions.
• This multi-scale approach is crucial for informed decision-making in epidemic management and policy development.