Enhancing mass vaccination programs with queueing theory and spatial optimization
2024

Improving Vaccination Programs with Queueing Theory

publication Evidence: high

Author Information

Author(s): Xie Sherrie, Rieders Maria, Changolkar Srisa, Bhattacharya Bhaswar B., Diaz Elvis W., Levy Michael Z., Castillo-Neyra Ricardo

Primary Institution: University of Pennsylvania

Hypothesis

Can integrating queueing theory into the placement of vaccination sites improve vaccine uptake?

Conclusion

Optimally placing mass vaccination sites by considering queueing can significantly reduce wait times and increase vaccination coverage.

Supporting Evidence

  • Queue-conscious site placement reduced attrition by 9-32%.
  • Vaccination coverage increased by 11-12% with optimized site placement.
  • Queue-naïve algorithms resulted in higher attrition rates.
  • Modeling queueing behavior improved vaccination outcomes even with imprecise parameters.

Takeaway

This study shows that if we think about how people wait in line for vaccinations, we can help more people get vaccinated faster.

Methodology

An algorithm was developed that combines queueing theory with spatial optimization to determine the best locations for vaccination sites.

Potential Biases

Potential bias in the model due to the assumption of constant arrival rates and the lack of consideration for socioeconomic factors.

Limitations

The study did not empirically estimate the parameters for queueing behavior and relied on hypothetical values.

Participant Demographics

The study focused on dog owners in Arequipa, Peru, but did not provide detailed demographic data.

Digital Object Identifier (DOI)

10.3389/fpubh.2024.1440673

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