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Optimization methods for decision making in disease prevention and epidemic control.

Yan Deng1, Siqian Shen, Yevgeniy Vorobeychik

  • 1Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.

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|October 15, 2013
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Summary

This study optimizes disease prevention and epidemic control (DPEC) strategies by deciding which individuals to vaccinate and which locations to close to minimize infections. It models human behavior and location interactions to inform public health interventions.

Keywords:
0–1 Knapsack problemCompensatory behavior modelingDisease prevention and interventionDynamic programmingDynamic/static disease control

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Area of Science:

  • Operations Research
  • Public Health
  • Epidemiology

Background:

  • Disease prevention and epidemic control (DPEC) are critical public health challenges.
  • Stochastic nature of disease spread necessitates robust intervention strategies.
  • Optimizing interventions requires understanding human behavior and location interactions.

Purpose of the Study:

  • To develop and evaluate mathematical models for optimizing DPEC strategies.
  • To minimize the expected number of infected individuals through vaccination and location closures.
  • To compare different algorithmic approaches for solving DPEC problems.

Main Methods:

  • Formulation of two integer nonlinear programming models.
  • Representation of individual-location interactions using bipartite graphs.
  • Development of algorithms including greedy strategy, dynamic programming, and integer programming.

Main Results:

  • Models effectively optimize vaccination and location closure decisions.
  • Comparison of computational efficacy and solution quality across different algorithms.
  • Analysis of DPEC strategies using real-world behavioral data.

Conclusions:

  • The developed models provide valuable insights for DPEC policy.
  • Understanding compensatory behavior in location choices is crucial for effective interventions.
  • The study demonstrates the utility of operations research techniques in public health.