从非随机测试数据估计人口感染率:来自COVID-19大流行病的证据
David Benatia1, Raphael Godefroy2, Joshua Lewis2
1Department of Applied Economics, HEC Montréal, Quebec, Canada.
PloS one
|September 26, 2024
概括
决策者需要准确的传染病流行数据. 这项研究引入了一种方法,通过测试数据估计实时感染率,揭示了COVID-19大流行期间大量未被诊断的病例.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 准确的传染病监测对于有效的公共卫生反应至关重要.
- 估计疾病患病率的现有方法经常与及时数据和非随机测试偏差作斗争.
- 新出现的传染病疫情需要创新的方法来实时估计流行率.
研究的目的:
- 利用非随机测试数据开发和验证一种用于估计人口感染率的新方法.
- 量化美国COVID-19大流行初期未诊断感染的程度.
- 为决策者提供更准确的流行数据,以便他们做出明智的决策.
主要方法:
- 开发了一种统计方法,将观察到的阳性率与测试人口的大小进行比较.
- 将该方法应用于第一波大流行期间美国各州的每日COVID-19测试数据.
- 通过分析相对于测试数量的阳性率梯度来推断总人口感染.
主要成果:
- 估计在第一波期间,美国各州的广泛未诊断的COVID-19感染.
- 在全国范围内发现的每一个确诊的COVID-19病例平均有12例未被诊断的感染.
- 患病率估计与血清患病率调查,其他感染测量方法和过度死亡率数据有很强的一致性.
结论:
- 新的方法有效地从非随机测试数据中估计实时感染率.
- 在最初的流行病浪潮期间,COVID-19患病率的严重低估发生了.
- 准确的实时流行数据对于管理传染病爆发和为公共卫生政策提供信息至关重要.
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