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Identifying cost-effective dynamic policies to control epidemics.

Reza Yaesoubi1, Ted Cohen2

  • 1Health Policy and Management, Yale School of Public Health, 60 College Street, New Haven, 06520, CT, U.S.A.. reza.yaesoubi@yale.edu.

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Summary

This study introduces a mathematical model for dynamic health policies to control epidemics, optimizing interventions based on real-time data and resources. Dynamic policies outperform static ones by maximizing net health benefit for better epidemic management.

Keywords:
H1N1approximate dynamic programmingapproximate policy iterationdynamic resource allocationepidemicsinfluenza

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health Policy

Background:

  • Effective epidemic control requires adaptive strategies that respond to evolving data and resource constraints.
  • Static policies, pre-determining intervention sequences, may not be optimal in dynamic epidemic scenarios.

Purpose of the Study:

  • To develop a mathematical decision model for identifying dynamic health policies for epidemic control.
  • To optimize these dynamic policies for maximizing the population's net health benefit, considering health and economic outcomes.
  • To illustrate the application of dynamic policies in managing a novel viral pathogen, including intervention timing and vaccine prioritization.

Main Methods:

  • Development of a mathematical decision model for dynamic policy optimization.
  • Proposal of an algorithm to approximate dynamic policies that maximize net health benefit.
  • Application of the model to a novel viral pathogen scenario, addressing intervention and vaccination strategies.

Main Results:

  • Dynamic policies can be defined and optimized to adapt to epidemic progression and resource availability.
  • The proposed algorithm approximates policies that optimize net health benefit.
  • Demonstration that dynamic policies yield higher net health benefit compared to static policies in epidemic control.

Conclusions:

  • Dynamic health policies offer a superior approach to epidemic control compared to static policies.
  • Mathematical modeling provides a robust framework for optimizing adaptive public health interventions.
  • The developed model and algorithm can guide decision-making for effective epidemic management and resource allocation.