ArticleFrontiers in public health2024
Enhancing mass vaccination programs with queueing theory and spatial optimization.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Socioeconomic disparities and dog rabies: a retrospective analysis of high-spatial-resolution surveillance data from a Latin American city.Lancet regional health. Americas · 2025Article
- Neighborhood Socioeconomic Status and Dog-Mediated Rabies: Disparities in Incidence and Surveillance Effort in a Latin American City.medRxiv : the preprint server for health sciences · 2025Article
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7 authors.
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Abstract
Background: Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or Methods: We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass dog rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those used in a previous vaccination campaign, as well as to sites obtained from a queue-naïve version of the same algorithm. Results: Sites placed by the queue-conscious algorithm resulted in 9-32% less attrition and 11-12% higher vaccination coverage compared to previously used sites and 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm placed more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption. Conclusion: One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.
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