Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• Mitigating infectious disease spread, such as COVID-19, in indoor settings is crucial.
• Existing methods for assessing risk often rely on surveillance or tracking technologies.

Purpose of the Study:

• To develop and present an agent-based modeling framework for evaluating facility usage policies to reduce indoor outbreak probability.
• To enable realistic contact network computation and facility risk profiling without invasive monitoring.

Main Methods:

• An individual-based, spatially-resolved agent-based model with high temporal resolution (1s).
• Detailed incorporation of floor layouts, occupant schedules, and movement patterns.
• Python implementation available as an open-source platform.

Main Results:

• Demonstrative modeling shows varying outbreak risk among occupants based on facility layout and schedules.
• Outbreak drivers are facility-specific, indicating a need for customized mitigation strategies.
• Generic policies are less effective than tailored approaches.

Conclusions:

• Agent-based modeling provides a powerful tool for evaluating tailored infectious disease mitigation policies in indoor environments.
• Understanding facility-specific dynamics is key to effective outbreak prevention.
• The open-source framework supports informed decision-making for public health interventions.