Xie Sherrie, Rieders Maria, Changolkar Srisa, Bhattacharya Bhaswar B, Diaz Elvis W, Levy Michael Z, Castillo-Neyra Ricardo
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA.
Operations, Information and Decisions Department, The Wharton School, University of Pennsylvania.
medRxiv. 2024 Jul 9:2024.06.14.24308958. doi: 10.1101/2024.06.14.24308958.
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 , which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.
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 canine 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 obtained from a queue-naive version of the same algorithm.
Sites placed by the queue-conscious algorithm resulted in 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 favored placing 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.
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.
大规模疫苗接种是公共卫生应急准备与响应的基石。然而，疫苗接种点设置不当可能导致长队排起，这会使人们不愿排队接种，从而可能危及公共卫生目标的实现。排队论为模拟疫苗接种点的排队形成及其对疫苗接种率的影响提供了一个框架。
我们开发了一种算法，该算法将排队论整合到空间优化框架中，以优化大规模疫苗接种点的设置。该算法是利用秘鲁阿雷基帕大规模犬用狂犬病疫苗接种活动的数据构建和测试的。我们将使用考虑排队的算法优化后的接种点的预期疫苗接种覆盖率和排队损失（即损耗）与使用同一算法的不考虑排队的版本得到的结果进行了比较。
与不考虑排队的算法设置的接种点相比，考虑排队的算法设置的接种点损耗减少了9%-19%，疫苗接种覆盖率提高了1%-2%。与不考虑排队的算法相比，考虑排队的算法倾向于在人口密集地区设置更多接种点，以抵消高到达量，从而减少因过度排队造成的损失。这些结果对排队参数的错误设定或恒定到达率假设的放宽不敏感。
即使没有经验得出的排队参数，在优化大规模疫苗接种点的设置时也应考虑排队损失。由于过长等待时间对参与者满意度有负面影响，减少排队损耗预计还会产生下游效益，并提高后续大规模疫苗接种活动中的疫苗接种覆盖率。
