Department of Public Health Sciences, University of California Davis , Davis, California, USA.
Department of Mathematics, Purdue University , West Lafayette, Indiana, USA.
mSystems. 2023 Aug 31;8(4):e0001823. doi: 10.1128/msystems.00018-23. Epub 2023 Jul 25.
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. IMPORTANCE 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.
大规模临床检测的部署是控制严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 感染负担和 2019 年冠状病毒病 (COVID-19) 大流行规模的重要控制措施。随着大流行的发展，出现了新的预防和监测机制。疫苗接种计划、废水 (WW) 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降低也减少了支持决策的公共卫生数据的可用性。本文提出了一种顺序贝叶斯方法，通过自适应方案利用 WW 中 SARS-CoV-2 RNA 浓度来估计 COVID-19 检测阳性率 (TPR)，该方案结合了病毒动力学的变化。所提出的建模框架应用于加利福尼亚州两个 WW 处理厂的 WW 监测数据；戴维斯市和加利福尼亚大学戴维斯分校。使用疾病控制与预防中心 (CDC) 的低 (TPR<5%)、中 (TPR5%-8%)、高 (TPR8%-10%) 和高 (>10%) 传播阈值，使用 WW 数据计算 TPR 估计值。使用 WW 数据中的 TPR 估计值计算有效繁殖数估计值。该方法提供了对病毒进化动态的深入了解，并提供了一个分析框架，该框架结合了不同的数据源，以继续监测 COVID-19 趋势。这些结果可以为减轻未来爆发的负担提供公共卫生指导，因为新的变异株不断出现。重要性 我们提出了一种统计模型，将 WW 与 TPR 相关联，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。我们提出了一种自适应方案来模拟 COVID-19 大流行的非自治性质。TPR 通过贝叶斯序贯方法建模，使用 WW 中测量的 SARS-CoV-2 RNA 浓度作为协变量的贝塔回归模型。由此产生的模型允许我们根据 WW 测量值计算 TPR，并通过自适应方案纳入病毒传播动力学的变化。
