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大规模疫苗接种中心和全科诊所的疫苗接种能力建模:一项模拟研究。

Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study.

机构信息

Centre for Big Data Research in Health, UNSW Sydney, Level 2, AGSM (G27), Sydney, NSW, 2052, Australia.

South Western Sydney Clinical School, Faculty of Medicine & Health, UNSW Sydney, Sydney, Australia.

出版信息

BMC Health Serv Res. 2022 Aug 19;22(1):1059. doi: 10.1186/s12913-022-08447-8.

DOI:10.1186/s12913-022-08447-8
PMID:35986322
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9388987/
Abstract

BACKGROUND

COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery.

METHODS

We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions.

RESULTS

Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics.

CONCLUSIONS

With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.

摘要

背景

COVID-19 大规模疫苗接种计划给医疗服务带来了额外的负担。我们旨在建立疫苗接种点的排队过程模型,以提供服务。

方法

我们使用随机队列网络模型来模拟大型疫苗接种中心和小型全科诊所(GP)的排队动态。我们根据预约时间表、服务时间和人员配备的一系列假设来估计等待时间和日容量,并对这些模型进行压力测试,以评估需求增加和人员短缺的影响。我们还提供了一个互动应用程序,允许用户根据自己的假设探索疫苗接种管理。

结果

根据我们假设的服务时间,一个八小时的大型疫苗接种中心每天的吞吐量范围为 500 剂(小中心)到 1400 剂(大中心)。对于 GP 诊所,估计的每日吞吐量范围从小诊所的约 100 剂到大诊所的近 300 剂。情景分析显示,员工人数较多的站点对系统压力更具弹性,而大型疫苗接种中心比 GP 诊所更具弹性。

结论

随着对 COVID-19 加强针的持续需求,大规模疫苗接种可能是医疗服务的一个持续特征。不同的疫苗接种点对于覆盖不同人群和最大化覆盖率是有用的。随机队列网络提供了一种灵活且计算效率高的方法来模拟疫苗接种队列,并估计等待时间和日吞吐量,以为服务提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/df5aaae56cf3/12913_2022_8447_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/ed1ccb5b8de0/12913_2022_8447_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/a0f20973b3ca/12913_2022_8447_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/427fa909cd6c/12913_2022_8447_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/320f49a1d7b6/12913_2022_8447_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/df5aaae56cf3/12913_2022_8447_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/ed1ccb5b8de0/12913_2022_8447_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/a0f20973b3ca/12913_2022_8447_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/427fa909cd6c/12913_2022_8447_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/320f49a1d7b6/12913_2022_8447_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/88b0/9389748/df5aaae56cf3/12913_2022_8447_Fig5_HTML.jpg

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