通过队列理论和空间优化增强大规模疫苗接种计划
Sherrie Xie1, Maria Rieders2, Srisa Changolkar2
1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA.
medRxiv : the preprint server for health sciences
|July 1, 2024
概括
通过排队理论优化大规模疫苗接种地点的位置,可以减少等待时间,增加疫苗覆盖率. 这种方法最大限度地减少了消耗,确保更多的人在公共卫生紧急情况下接种疫苗.
科学领域:
- 公共卫生 公共卫生
- 运营研究 运营研究
- 流行病学 流行病学
背景情况:
- 大规模疫苗接种对于公共卫生紧急响应至关重要.
- 疫苗接种地点的位置不理想,可能会导致长时间排队,阻碍接种疫苗并阻碍公共卫生目标.
- 队列理论可以模拟队列动态及其对疫苗接种的影响.
研究的目的:
- 开发和评估一个算法,以优化大规模疫苗接种地点的配置.
- 将队列理论与空间优化整合在一起,以尽量减少接种地点的队列,并最大限度地覆盖疫苗.
- 使用现实世界的数据,将队列意识的算法与队列天真的方法进行比较.
主要方法:
- 开发了一种结合排队理论和空间优化用于选址的新算法.
- 使用来自秘鲁阿雷基帕的大规模犬狂犬病疫苗接种活动的数据测试了算法.
- 排队意识和排队无知的放置策略之间的疫苗接种覆盖率和消耗率的比较.
主要成果:
- 排队意识的网站放置导致排队减少了9-19%,疫苗接种覆盖率提高了1-2%.
- 优化的算法优先考虑人口密集地区,以管理高抵达量并减少排队损失.
- 尽管排队参数和抵达率假设的变化,但结果仍然强.
结论:
- 整合排队动态对于最佳的大规模疫苗接种地点的配置至关重要,即使没有精确的参数数据.
- 减少排队消耗会提高参与者的满意度,并预计将提高未来疫苗接种活动的覆盖率.
- 这种队列感知空间优化框架为改善公共卫生干预提供了宝贵的工具.
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