Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater.
Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater.
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DOI：
10.1128/msystems.00018-23
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发表时间：
2023-08-31
期刊：
影响因子：
6.4
通讯作者：
中科院分区：
文献类型：
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作者：
大规模部署临床检测是减少严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染负担和减少COVID-19（冠状病毒病2019）大流行期间规模的必要控制措施。随着大流行的发展，出现了新的预防和监测机制。疫苗规划、废水监测和家庭COVID-19抗原检测的实施减少了对大规模SARS-CoV-2检测的需求。不幸的是，检测和检测报告率的下降也减少了支持决策的公共卫生数据的可得性。本文提出了一种序列贝叶斯方法，通过结合病毒动力学变化的自适应方案，利用WW中测量的SARS-CoV-2 RNA浓度来估计COVID-19检测阳性率（TPR）。将提出的建模框架应用于加利福尼亚州两个污水处理厂的污水监测数据；戴维斯市和加州大学戴维斯分校校园。TPR估计值用于使用疾病控制和预防中心的低（<5% TPR）、中等（5% - 8% TPR）、大量（8%-10% TPR）和高（10% TPR）传播阈值来计算WW数据的阈值。利用WW数据的TPR估计计算有效繁殖数。这种方法提供了对病毒演变动态的见解，并提供了一个结合不同数据源的分析框架，以继续监测COVID-19趋势。随着新变种的不断出现，这些结果可为减轻未来疫情的负担提供公共卫生指导。我们提出了一个统计模型，将WW与TPR关联起来，以监测COVID-19趋势，并帮助克服仅依赖临床病例检测的局限性。我们提出了一个自适应方案来模拟延长的COVID-19大流行的非自治性质。TPR通过贝叶斯序列方法和β回归模型建模，以WW测量的SARS-CoV-2 RNA浓度为协变量。由此产生的模型使我们能够计算基于WW测量的TPR，并通过自适应方案纳入病毒传播动力学的变化。
Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%–8% TPR), substantial (8%–10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.
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影响因子：
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作者：
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DOI：
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影响因子：
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DOI：
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期刊：
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SARS-COV-2废水流行病学中的生物标志物选择人群归一化。
影响因子：
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作者：
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Lin, Chung-Ho