通过重复的聚合测试对SARS-CoV-2流行情况的监测:适用于瑞士常规数据
Julien Riou1,2, Erik Studer3, Anna Fesser3
1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Epidemiology and infection
|August 21, 2024
概括
聚合测试为SARS-CoV-2监测提供了一种可靠且负担得起的方法,纠正了传统测试中的偏见. 这种方法准确地估计了不同环境中的感染流行率.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 传染病监测 传染病监测 传染病
背景情况:
- 使用报告的阳性RT-PCR测试进行SARS-CoV-2的常规监测,由于非随机测试模式,可能会有显著的偏差.
- 基于人口的抽样方法对于准确的流行率估计至关重要,聚合测试提供了一个有希望但在分析上具有挑战性的方法.
研究的目的:
- 开发和验证贝叶斯模型,通过重复的聚合测试数据来估计SARS-CoV-2的流行率.
- 该模型旨在纠正测试灵敏度,考虑灵敏度的不确定性,并纳入时间和空间的相关性.
主要方法:
- 开发了贝叶斯统计模型来分析重复聚合的测试数据.
- 模型验证使用模拟场景进行,采用不同样本大小,池大小和流行率的模拟场景.
- 该模型应用于来自瑞士的149万个综合测试 (2021-2022) 的大型数据集.
主要成果:
- 该模型在样本大小≥500,池大小<20和患病率<5%的情况下显示出可靠性.
- 对瑞士数据的分析显示,学校,护理中心和工作场所的流行动态类似,在2022年冬季达到4-5%的峰值.
- 学校的流行率估计显示,与报告的病例,住院和死亡 (0.840.90) 有很强的相关性.
结论:
- 聚合测试,当与适当的统计模型进行分析时,为SARS-CoV-2监测提供了可靠和具有成本效益的替代方案.
- 开发的贝叶斯模型有效地解决了与聚合测试数据相关的分析挑战.
- 这种方法对SARS-CoV-2和潜在的其他传染病的监测有影响.
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