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Studying COVID-19 transmission in US state prisons using an agent-based modelling approach: a simulation study.

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Area of Science:

  • Epidemiology
  • Public Health
  • Computational Modeling

Background:

  • COVID-19 significantly impacted incarcerated populations, necessitating better predictive models.
  • Existing models lacked prison-specific factors like capacity and vaccination rates, limiting generalizability.
  • This study addresses the need for models tailored to the unique systemic factors within correctional facilities.

Purpose of the Study:

  • To develop and validate an agent-based model for accurate COVID-19 spread prediction in US state prisons.
  • To incorporate prison-specific characteristics, including vaccination rates and overcrowding, into disease transmission models.
  • To provide actionable insights for mitigating infectious disease outbreaks in correctional settings.

Main Methods:

  • Developed a semistochastic agent-based model incorporating geospatial contact networks and compartmental dynamics.
  • Simulated COVID-19 outbreaks in five North Carolina prisons (July 2020-June 2021) using facility capacity and vaccination rates.
  • Utilized approximate Bayesian computation for parameter estimation and model fitting to real-world data.

Main Results:

  • The model achieved a mean absolute percentage error (MAPE) of 23.0, indicating reasonable prediction accuracy for infection and recovery rates.
  • Estimated average vaccination rate at 54% and facility occupancy at 90%, suggesting insufficient vaccination and high overcrowding.
  • Identified data gaps as a significant barrier to accurate outbreak prediction, emphasizing the need for consistent data reporting.

Conclusions:

  • Spatial contact networks and facility characteristics are crucial for predicting infectious disease spread in congregate settings.
  • Increased vaccination efforts and potential capacity reductions are recommended to mitigate COVID-19 transmission in prisons.
  • The study underscores the importance of robust data collection for effective public health interventions in correctional facilities.