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COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina.
Erik T Rosenstrom1, Julie S Ivy2, Maria E Mayorga3
1Operations Research, North Carolina State University, Raleigh, USA.
Epidemics
|February 29, 2024
Summary
We developed COVSIM, an agent-based model, to analyze COVID-19 spread influenced by behaviors and policies across diverse North Carolina populations. This simulation aids public health decisions and research on interventions and equity.
Area of Science:
- Epidemiology
- Computational modeling
- Public health
Background:
- COVID-19 spread is complex, influenced by population behaviors and policy interventions.
- Understanding disease dynamics across diverse subpopulations is crucial for effective public health strategies.
- Agent-based modeling offers a powerful tool to simulate these complex interactions.
Purpose of the Study:
- To document the evolution and application of the stochastic agent-based COVID-19 simulation model (COVSIM).
- To assess the impact of population behaviors and public health policies on COVID-19 transmission.
- To analyze disease spread within specific subpopulations in North Carolina, considering age, race/ethnicity, and urbanicity.
Main Methods:
- Developed a stochastic agent-based model (COVSIM) incorporating agent attributes (age, race/ethnicity, medical status), interaction networks, disease states, and behaviors (masking, vaccination, quarantine, mobility).
- Modeled the influence of COVID-19 variants and pharmaceutical interventions (PIs).
- Integrated the model into the COVID-19 Scenario Modeling Hub (CSMH) for enhanced analysis and computational efficiency.
Main Results:
- COVSIM has been utilized to study the interplay of nonpharmaceutical and pharmaceutical interventions.
- The model has supported analyses on the equitability of vaccine distribution.
- COVSIM has provided valuable support to local county decision-makers in North Carolina.
Conclusions:
- The COVSIM model provides a robust framework for understanding COVID-19 transmission dynamics.
- Its integration with the CSMH facilitated a sustainable approach to addressing emerging COVID-19 challenges.
- The model's adaptability has led to improvements in computational implementation and supports broader scientific inquiry.
Keywords:
COVID-19 scenario modeling hubHealth equityInfectious disease modelingLocal public health decision makingStochastic agent-based simulationMore Related Videos
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