COVID-19的活动流行率是多少？

Review of Economics and Statistics · 2023 · [{"name": "Mu‐Jeung Yang", "affiliation": ["University of Oklahoma"]}, {"name": "Marinho Bertanha", "affiliation": ["University of Notre Dame"]}, {"name": "Nathan Seegert", "affiliation": ["University of Utah"]}, {"name": "Maclean Gaulin", "affiliation": ["University of Utah"]}, {"name": "Adam Looney", "affiliation": ["University of Utah"]}, {"name": "Brian Orleans", "affiliation": ["University of Utah"]}, {"name": "Andrew T. Pavia", "affiliation": ["University of Utah"]}, {"name": "Kristina Stratford", "affiliation": ["University of Utah"]}, {"name": "Matthew H. Samore", "affiliation": ["University of Utah"]}, {"name": "Steven Alder", "affiliation": ["University of Utah"]}]

中文摘要

我们提供一种实时追踪COVID-19活动流行率的方法，该方法对基于症状的检测数据中随时间变化的样本选择问题，以及康复病例和死亡病例追踪不完整的问题进行了校正。该方法仅需将公开可得的检测阳性率数据与一个参数相结合；我们依据2020年5月和6月在犹他州对近10,000人进行检测所得的代表性随机样本估计这一参数。我们使用2020年4月印第安纳州的外部研究，以及2021年3月犹他州两个县的外部研究对该方法进行了验证。在这三个地点和时间点，我们对潜在流行率的估计均处于随机检测所得流行率估计的95%置信区间内。将该方法应用于美国全部50个州后，我们发现，真实流行率是公开报告水平的2至3倍。

Abstract

Abstract We provide a method to track the active prevalence of COVID-19 in real time, correcting for time-varying sample selection in symptom-based testing data and incomplete tracking of recovered cases and fatalities. Our method only requires publicly available data on positive testing rates in combination with one parameter, which we estimate based on a representative randomized sample of nearly 10,000 individuals tested in Utah in May and June 2020. We validate our method using external studies in Indiana in April 2020 and two counties in Utah in March 2021. In all three locations and times, our estimates of latent prevalence are within the 95 percent confidence intervals of prevalence estimates from randomized testing. Applying our method to all 50 states, we show that true prevalence is 2–3 times higher than publicly reported.