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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.

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Summary

Optimizing mass vaccination site placement using queueing theory reduces wait times and increases vaccine coverage. This approach minimizes attrition, ensuring more individuals receive vaccinations during public health emergencies.

Keywords:
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Area of Science:

  • Public Health
  • Operations Research
  • Epidemiology

Background:

  • Mass vaccination is critical for public health emergency response.
  • Suboptimal vaccination site placement can cause long queues, deterring vaccination and hindering public health goals.
  • Queueing theory can model queue dynamics and their impact on vaccine uptake.

Purpose of the Study:

  • To develop and evaluate an algorithm for optimizing mass vaccination site placement.
  • To integrate queueing theory with spatial optimization to minimize vaccination site queues and maximize coverage.
  • To compare a queue-conscious algorithm with a queue-naïve approach using real-world data.

Main Methods:

  • Developed a novel algorithm combining queueing theory and spatial optimization for site selection.
  • Tested the algorithm using data from a mass canine rabies vaccination campaign in Arequipa, Peru.
  • Compared vaccination coverage and attrition rates between queue-conscious and queue-naïve placement strategies.

Main Results:

  • Queue-conscious site placement led to 9-19% less attrition and 1-2% higher vaccination coverage.
  • The optimized algorithm prioritized densely populated areas to manage high arrival volumes and reduce queueing losses.
  • Results remained robust despite variations in queueing parameters and arrival rate assumptions.

Conclusions:

  • Incorporating queueing dynamics is essential for optimal mass vaccination site placement, even without precise parameter data.
  • Reducing queueing attrition improves participant satisfaction and is expected to enhance future vaccination campaign coverage.
  • This queue-aware spatial optimization framework offers a valuable tool for improving public health interventions.