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A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management.

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Summary

This study introduces an efficient algorithm to optimize healthcare interventions for population disease management, improving health outcomes under budget constraints. The Multi-Fidelity Rollout Algorithm (MF-RA) effectively balances resource allocation for screening and treatment, demonstrating scalability for complex public health challenges like Hepatitis C virus elimination.

Keywords:
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Area of Science:

  • Public Health
  • Health Economics
  • Computational Biology

Background:

  • Healthcare costs are rising, necessitating efficient resource allocation for population disease management.
  • Optimizing sequential interventions for prevention, screening, and treatment is crucial for improving health outcomes.
  • Dynamic decision-making in population health often faces computational challenges due to large state spaces and multiple time periods.

Purpose of the Study:

  • To design a model and algorithm for optimizing sequential intervention policies under resource constraints.
  • To improve population health outcomes by addressing dynamic budget allocation for disease management.
  • To develop an efficient method for identifying non-stationary sequential intervention policies.

Main Methods:

  • Developed a low-fidelity approximation to preserve population dynamics under stationary policies.
  • Embedded the low-fidelity approximation into a high-fidelity optimization model.
  • Proposed the Multi-Fidelity Rollout Algorithm (MF-RA) for efficient policy identification.
  • Utilized a discrete-time finite-horizon budget allocation problem framework.
  • Applied the model to chronic Hepatitis C virus (HCV) screening and treatment policy.

Main Results:

  • The MF-RA efficiently identifies effective non-stationary sequential intervention policies.
  • Numerical comparisons show MF-RA yields similar policy trends and health outcomes (QALYs) to grid search.
  • The algorithm demonstrates scalability for high-dimensional problems, as shown in HCV elimination time analysis.
  • Achieved close overall health outcomes and intervention numbers across different budgets and cohorts.

Conclusions:

  • The MF-RA provides an efficient approach to optimize dynamic resource allocation for population disease management.
  • This method effectively balances healthcare interventions to improve population health outcomes under budget constraints.
  • The approach is scalable and applicable to complex public health challenges, including disease elimination strategies.