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High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town
Agnieszka Truszkowska1, Brandon Behring1, Jalil Hasanyan1
1Department of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA.
Advanced Theory and Simulations
|March 31, 2021
Summary
This study introduces an agent-based model to simulate COVID-19 spread in communities. The tool aids public health in evaluating testing and vaccination strategies for better pandemic control.
Area of Science:
- Epidemiology
- Computational modeling
- Public health
Background:
- The COVID-19 pandemic necessitates advanced tools for evaluating public health interventions.
- Balancing safety and normalcy requires understanding disease spread dynamics in diverse populations.
Purpose of the Study:
- To develop and validate an agent-based modeling platform for simulating COVID-19 transmission.
- To assess the effectiveness of various testing and vaccination strategies.
Main Methods:
- An agent-based model simulating individual-level disease spread was developed.
- The model was validated using real-world data from New Rochelle, NY.
- It incorporates testing, treatment, vaccination, and co-morbidity factors.
Main Results:
- The platform provides fine-grain resolution for simulating disease spread in small towns and cities.
- It allows exploration of different testing (hospital, drive-through) and vaccination strategies.
- The model accounts for other illnesses with similar symptoms to COVID-19.
Conclusions:
- This open-source agent-based model offers a valuable tool for public health decision-making.
- Its features support "what-if" scenario analysis for pandemic response planning.
- The model aids in prioritizing vulnerable groups for vaccination and optimizing testing approaches.
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