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A decision-support framework to optimize border control for global outbreak mitigation.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Emerging infectious diseases are increasing in prevalence and scale.
  • Uncertainty in virus origins and spread necessitates real-time, validated outbreak control policies.
  • Passenger air travel facilitates rapid global disease transmission.

Purpose of the Study:

  • To develop a novel mathematical modeling framework for mitigating infectious disease pandemics.
  • To propose and evaluate an ensemble of border control strategies using air traffic network properties.
  • To provide a decision support tool for real-time outbreak control.

Main Methods:

  • Development of a stochastic metapopulation epidemic model.
  • Integration of outbreak dynamics and control strategies within the model.
  • Evaluation and ranking of control strategies based on spread reduction.
  • Sensitivity analyses across various outbreak scenarios.
  • Case study using the 2009 H1N1 influenza pandemic.

Main Results:

  • The study proposes an ensemble of border control strategies tailored to air traffic networks.
  • The stochastic metapopulation model effectively evaluates and ranks control strategy performance.
  • Sensitivity analyses demonstrate the robustness of proposed strategies across diverse scenarios.
  • A case study on the 2009 H1N1 pandemic validates the framework's applicability.

Conclusions:

  • Strategic allocation of outbreak control resources is crucial for pandemic mitigation.
  • The developed framework identifies robust border control policies for early-stage outbreak response.
  • The findings support real-time implementation of validated control measures to reduce disease spread.