Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, United States of America.
Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, College Station, Texas, United States of America.
PLoS Comput Biol. 2021 Sep 7;17(9):e1009374. doi: 10.1371/journal.pcbi.1009374. eCollection 2021 Sep.
Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic.
准确估计感染率和血清流行率对于评估和指导美国每个州的公共卫生应对措施以及为解决 COVID-19 的持续传播所需的疫苗接种覆盖率至关重要。然而，基于代表性人群抽样的可靠、及时的数据不可用，报告的病例和检测阳性率存在严重偏差。本研究开发了一个简单的数据驱动贝叶斯半经验建模框架，并用于使用每日报告的病例和检测阳性率评估州级 COVID-19 的流行率和血清流行率。该模型经过校准和验证，使用了已发表的全州血清流行率数据，并与两个独立的数据驱动数学模型进行了比较。研究发现，未确诊 COVID-19 感染的流行率可以通过阳性率和报告病例率的几何加权平均值很好地近似。我们的模型准确拟合了美国各地的州级血清流行率数据。我们的半经验模型的流行率估计与两个数据驱动的流行病学模型的估计相比具有优势。截至 2020 年 12 月 31 日，我们估计全国的流行率为 1.4%[置信区间（CrI）：1.0%-1.9%]和血清流行率为 13.2%[CrI：12.3%-14.2%]，州级流行率范围从夏威夷的 0.2%[CrI：0.1%-0.3%]到田纳西州的 2.8%[CrI：1.8%-4.1%]，血清流行率从佛蒙特州的 1.5%[CrI：1.2%-2.0%]到纽约州的 23%[CrI：20%-28%]。总的来说，报告的病例仅占实际感染的三分之一。使用这种简单易用的方法来估计 COVID-19 的流行率和血清流行率将提高做出公共卫生决策的能力，从而有效地应对持续的 COVID-19 大流行。
