An agent-based model to evaluate the COVID-19 transmission risks in facilities

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The COVID-19 pandemic necessitates strategies for safe reopening of facilities.
• Mathematical models are crucial for assessing transmission risks and informing public health decisions.
• Agent-based modeling offers a more realistic approach than traditional models by simulating individual behaviors.

Purpose of the Study:

• To introduce an agent-based model for evaluating COVID-19 transmission risks in various facilities.
• To simulate the spatiotemporal transmission dynamics of the virus within defined environments.
• To provide a tool for authorities to make informed decisions regarding facility operations.

Main Methods:

• Development of an agent-based model simulating individual agents with unique profiles (social characteristics, health conditions).
• Programming agent decision-making rules based on spatial patterns and infection conditions for interaction.
• Simulation of hypothetical scenarios to demonstrate the model's performance in evaluating transmission risks.

Main Results:

• The agent-based model effectively simulates the spatiotemporal transmission of COVID-19.
• Simulations provide insights into how individual agent characteristics influence transmission dynamics.
• The model's outputs are valuable for developing targeted risk reduction strategies within facilities.

Conclusions:

• Agent-based modeling is a powerful tool for understanding and mitigating COVID-19 spread in specific settings.
• The proposed model can assist in creating safer environments by informing policy and operational adjustments.
• This approach supports a transition to a 'new normal' by managing infection risks in public and private facilities.