使用受访者驱动的抽样方法估计COVID-19的隐藏人口规模 - 一个系统性审查
SeyedAhmad SeyedAlinaghi1, Arian Afzalian2, Mohsen Dashti3
1Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran.
Infectious disorders drug targets
|February 1, 2024
概括
受访者驱动采样 (RDS) 有效估计了隐藏的COVID-19病例,揭示了比报告的感染人数要高得多. 这种具有成本效益的方法对于未来的疫情准备至关重要.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 统计方法 统计方法
背景情况:
- 由于无症状携带者,COVID-19流行病构成了全球卫生挑战,感染人数往往被低估.
- 准确检测COVID-19病例对于有效的治疗和预防策略至关重要.
- 传统的采样方法不足以达到隐藏或难以到达的种群.
研究的目的:
- 为了估计隐藏的COVID-19感染人口的规模.
- 为此目的评估受访者驱动抽样 (RDS) 的实用性.
主要方法:
- 2019年12月至2022年12月期间发表的英语文章的系统审查.
- 在主要数据库中进行的搜索:PubMed,科学网,Scopus,Embase和Cochrane.
- 分析了7项精选的研究,重点关注COVID-19的流行率和未被发现病例的估计.
主要成果:
- 所有7项包括旨在估计COVID-19患病率和模型无症状/未检测到病例的研究.
- 两项研究报告的患病率为2.6% (塞拉利昂) 和2.4% (奥地利).
- 四项研究表明,实际感染人数是官方报告的2至50倍.
结论:
- 受访者驱动采样 (RDS) 在估计未检测到的无症状COVID-19病例方面表现出有效性.
- RDS提供了一种具有成本效益,低成本和相对无故障的采样方法.
- 该方法对于管理可能的未来流行病具有重要价值.
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