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Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration.

Alexandra Cimpean1, Timothy Verstraeten2, Lander Willem3,4

  • 1Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium. ioana.alexandra.cimpean@vub.be.

Scientific Reports
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Summary

This study introduces a new Bayesian method using multi-armed bandits to find optimal COVID-19 vaccine allocation strategies, efficiently identifying top policies to minimize infections and hospitalizations.

Keywords:
COVID-19Individual-based modelsM-top anytime decision makingMulti-armed banditsVaccine policies

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health Policy

Background:

  • Individual-based epidemiological models are crucial for simulating fine-grained public health interventions like vaccine allocation.
  • These models are computationally intensive, necessitating efficient strategies for identifying optimal policies within budget constraints.
  • Communicating decision uncertainty to policymakers is vital for high-impact public health strategies, aligning with Bayesian approaches.

Purpose of the Study:

  • To develop and evaluate a novel technique for optimizing vaccine allocation strategies using a multi-armed bandit framework.
  • To integrate a Bayesian anytime m-top exploration algorithm for efficient identification and uncertainty quantification of top-performing policies.
  • To provide policy advisors with flexible computational options and confidence levels for decision-making.

Main Methods:

  • Utilized a multi-armed bandit framework where each unique vaccine allocation policy was defined as an 'arm'.
  • Implemented a Bayesian anytime m-top exploration algorithm to identify the 'm' policies with the highest expected utility.
  • Applied the method to the Belgian COVID-19 epidemic using the STRIDE individual-based model to minimize infections and hospitalizations.

Main Results:

  • The proposed method efficiently identified the top 'm' vaccine allocation policies.
  • Analysis revealed clear trends in prioritized age groups and vaccine types within the optimal policies.
  • Vaccination policies were explored under various social contact reduction scenarios and vaccine uptake proportions.

Conclusions:

  • The developed technique effectively identifies optimal vaccine allocation strategies and quantifies associated uncertainties.
  • Findings offer valuable insights for designing future vaccination campaigns, highlighting age and vaccine type prioritization.
  • Vaccine uptake proportion demonstrated a limited impact on the overall optimality of the identified strategies.