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Enhancing mass vaccination programs with queueing theory and spatial optimization.

Sherrie Xie1, Maria Rieders2, Srisa Changolkar2

  • 1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, United States.

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Summary

Optimizing mass vaccination site placement using queueing theory significantly reduces wait times and increases vaccine coverage. This approach improves public health emergency response by minimizing vaccine loss due to long queues.

Keywords:
One Healthemergency preparednessfacility locationmass vaccinationqueueing theoryrabiesspatial optimizationzoonosis

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Area of Science:

  • Public Health
  • Operations Research
  • Epidemiology

Background:

  • Mass vaccination is crucial for public health emergencies.
  • Poorly placed vaccination sites cause long queues, reducing vaccine uptake.
  • Queueing theory can model and mitigate these issues.

Purpose of the Study:

  • To develop and evaluate an algorithm for optimizing mass vaccination site placement using queueing theory.
  • To compare the effectiveness of queue-conscious site placement against traditional methods.

Main Methods:

  • Developed a spatial optimization algorithm integrating queueing theory.
  • Tested the algorithm using data from a mass dog rabies vaccination campaign in Arequipa, Peru.
  • Compared outcomes (vaccination coverage, attrition) with previous campaign sites and a queue-naïve algorithm.

Main Results:

  • Queue-conscious placement reduced attrition by 9-32% and increased coverage by 11-12% compared to previous sites.
  • It also reduced attrition by 9-19% and increased coverage by 1-2% versus a queue-naïve algorithm.
  • The algorithm prioritized densely populated areas to manage high arrival volumes.

Conclusions:

  • Queueing losses must be considered for optimal mass vaccination site placement, even without precise queueing data.
  • Reducing queueing attrition improves participant satisfaction and future vaccination campaign success.