Department of Applied Economics, HEC Montréal, Quebec, Canada.
Department of Economics, Université de Montréal, Quebec, Canada.
PLoS One. 2024 Sep 26;19(9):e0311001. doi: 10.1371/journal.pone.0311001. eCollection 2024.
To effectively respond to an emerging infectious disease outbreak, policymakers need timely and accurate measures of disease prevalence in the general population. This paper presents a new methodology to estimate real-time population infection rates from non-random testing data. The approach compares how the observed positivity rate varies with the size of the tested population and applies this gradient to infer total population infections. Applying this methodology to daily testing data across U.S. states during the first wave of the COVID-19 pandemic, we estimated widespread undiagnosed COVID-19 infections. Nationwide, we found that for every identified case, there were 12 population infections. Our prevalence estimates align with results from seroprevalence surveys, alternate approaches to measuring COVID-19 infections, and total excess mortality during the first wave of the pandemic.
为了有效应对新发传染病的爆发，政策制定者需要及时、准确地掌握一般人群中疾病的流行情况。本文提出了一种新的方法，可利用非随机检测数据来估算实时人群感染率。该方法通过比较观察到的阳性率随检测人群规模的变化，并利用这种梯度来推断总人群感染率。将该方法应用于美国 COVID-19 大流行第一波期间各州的每日检测数据，我们估算出广泛存在的未确诊 COVID-19 感染病例。在全国范围内，我们发现每确诊一例病例，就有 12 例人群感染。我们的流行率估计与血清流行率调查、衡量 COVID-19 感染的替代方法以及大流行第一波期间的总超额死亡率的结果一致。
