Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States.
Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States.
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DOI：
10.1371/journal.pcbi.1009374
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发表时间：
2021-09
影响因子：
4.3
通讯作者：
Ndeffo-Mbah ML
中科院分区：
文献类型：
--
作者：
Chiu WA;Ndeffo-Mbah ML
准确估计感染流行率和血清流行率对于评估和告知公共卫生应对措施和疫苗接种覆盖率至关重要，以应对 COVID-19 在美国各州的持续传播。然而，无法获得基于代表性人口抽样的可靠、及时的数据，并且报告的病例和检测阳性率存在很大偏差。开发了一个简单的数据驱动的贝叶斯半经验建模框架，并使用每日报告的病例和测试阳性率来评估州级的 COVID-19 患病率和血清阳性率。该模型使用已发布的全州血清流行率数据进行校准和验证，并进一步与两个独立的数据驱动数学模型进行比较。未确诊的 COVID-19 感染的患病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。我们的模型准确地拟合了美国各地州级血清流行率数据。我们的半经验模型的流行率估计优于两个数据驱动的流行病学模型的流行率估计。截至 2020 年 12 月 31 日，我们估计全国范围内的患病率为 1.4% [可信区间 (CrI)：1.0%-1.9%]，血清患病率为 13.2% [CrI：12.3%-14.2%]，州级患病率为夏威夷的 0.2% [CrI：0.1%-0.3%] 到 2.8%田纳西州的 [CrI：1.8%-4.1%]，血清阳性率从佛蒙特州的 1.5% [CrI：1.2%-2.0%] 增至纽约的 23% [CrI：20%-28%]。累计报告的病例仅相当于实际感染病例的三分之一。使用这种简单且易于沟通的方法来估计 COVID-19 流行率和血清流行率将提高做出公共卫生决策的能力，从而有效应对持续的 COVID-19 大流行。及时、可靠地估计 COVID-19 流行率和血清流行率对于评估美国各州大流行的传播和控制至关重要。单独依赖报告的病例和检测阳性率可能会导致对 COVID-19 传播的错误推断以及不知情的公共卫生决策。我们的研究开发了一个简单的半经验模型，用于使用报告的病例和检测阳性率数据来估计美国 (US) 州级 COVID-19 的患病率和血清阳性率。我们发现，由于美国诊断性 COVID-19 检测的优先性，报告病例和检测阳性率的几何平均值可以准确预测未确诊的 COVID-19 患病率和趋势。
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. Timely and reliable estimates of COVID-19 prevalence and seroprevalence are paramount for evaluating the spread and control of the pandemic in different US states. Relying on reported cases and test positivity rates individually can result in incorrect inferences as to the spread of COVID-19 and ill-informed public health decision-making. Our study developed a simple semi-empirical model for estimating state-level prevalence and seroprevalence of COVID-19 in the United States (US) using reported case and test positivity rates data. We found that due to the preferential nature of diagnostic COVID-19 testing in the US, the geometric mean of reported case and test positivity rates is an accurate predictor of undiagnosed COVID-19 prevalence and trends.
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影响因子：
2.7
作者：
Hartwell M;Greiner B;Kilburn Z;Ottwell R
通讯作者：
Ottwell R
DOI：
10.3390/s21072435
发表时间：
2021-04-01
期刊：
Sensors (Basel, Switzerland)
影响因子：
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作者：
Fenga L;Gaspari M
通讯作者：
Gaspari M
DOI：
10.1016/s0140-6736(20)32009-2
发表时间：
2020-10-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Anand S;Montez-Rath M;Han J;Bozeman J;Kerschmann R;Beyer P;Parsonnet J;Chertow GM
通讯作者：
Chertow GM
DOI：
10.3390/ijerph18094655
发表时间：
2021-04-27
影响因子：
--
作者：
Furuse Y;Ko YK;Ninomiya K;Suzuki M;Oshitani H
通讯作者：
Oshitani H
影响因子：
2.4
作者：
Brooks, SP;Gelman, A
通讯作者：
Gelman, A