SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model.
SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model.
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使用分布式滞后模型对 SARS-CoV-2 RNA 废水沉降固体进行监测频率以及对预测的 COVID-19 发病率的影响。
DOI：
10.1021/acsestwater.2c00074
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发表时间：
2022-11-11
期刊：
影响因子：
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通讯作者：
Boehm, Alexandria B
中科院分区：
文献类型：
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作者：
Schoen, Mary E;Wolfe, Marlene K;Li, Linlin;Duong, Dorothea;White, Bradley J;Hughes, Bridgette;Boehm, Alexandria B
废水沉降固体中的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）RNA浓度与2019年冠状病毒病（COVID-19）发病率（IR）密切相关。在这里，我们开发了分布式滞后模型，估计IR使用浓度的SARS-CoV-2 RNA废水固体和调查的采样频率对模型性能的影响。每天在加州的四个废水处理厂测量SARS-CoV-2 N基因和辣椒轻度斑驳病毒（PMMoV）RNA浓度。每种植物的采样频率为每2、3、4和7天一次，产生了简化的数据集。将每日N/PMMoV与IR相关联的下水道特定模型适用于2020年11月中旬至2021年7月中旬的每个采样频率，其中包括Delta出现的时间段。使用模型预测随后样本外时间段内的IR。当采样至少每4天进行一次时，与污水处理厂每天采样相比，样本内和样本外的均方根误差变化<7例/100 000。这项工作表明，实时，每日预测的IR是可能的，误差很小，尽管在循环变量的变化，采样频率是每4天或更长时间。然而，降低采样频率可能无法用于其他重要的废水监测用例。分布式滞后模型使用废水固体监测SARS-CoV-2 N基因预测COVID-19发病率，误差相对较小。
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and pepper mild mottle virus (PMMoV) RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced data sets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-November 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root-mean-square error changed by <7 cases/100 000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small errors, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases. Distributed lag models predicted COVID-19 incidence rates with relatively small errors using wastewater solids surveillance of SARS-CoV-2 N genes.