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Pandemic policy assessment by artificial intelligence.

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This study introduces an AI-powered system to assess pandemic mobility control policies. Community quarantine offers a balanced approach, effectively reducing infections with manageable complexity.

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Area of Science:

  • Epidemiology
  • Computational Science
  • Public Health Policy

Background:

  • Mobility-control policies are crucial for pandemic management but face challenges due to liberty restrictions and economic impacts.
  • Assessing and comparing diverse mobility-control strategies is computationally complex, hindering effective policy selection.
  • Existing methods struggle to quantitatively evaluate the trade-offs between pandemic control and societal disruption.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) system for evaluating and analyzing various mobility-control policies during pandemics.
  • To provide a framework for comparing different pandemic control strategies based on execution complexity, effectiveness, cost-benefit, and risk.
  • To generate mobility-pandemic trade-off frontiers for popular policies using real-world data.

Main Methods:

  • Development of a pandemic policy assessment system integrating a simulation framework, a reinforcement-learning (RL) oracle, and policy-assessment protocols.
  • Modeling mobility-control policies as network-flow control problems within a general simulation framework.
  • Utilizing RL to determine upper-bound execution results and converting these to quantifiable policy-assessment measures.

Main Results:

  • Evaluation of city lockdown, community quarantine, and route management policies using real-world metropolitan data.
  • Generation of mobility-pandemic trade-off frontiers, revealing that highly complex policies like route management offer limited additional mobility retention benefits.
  • Community quarantine demonstrated a favorable balance, effectively suppressing infections with acceptable execution complexity and mitigating mobility restrictions.

Conclusions:

  • The AI system provides a robust method for assessing pandemic control policies, offering insights into optimal strategies.
  • Community quarantine emerges as a potentially effective policy balancing infection control with socioeconomic considerations.
  • The findings offer valuable guidance for ongoing policy debates, including zero-COVID strategies, vaccination policies, and restriction relaxation.