Ravuri Sindhu, Burnor Elisabeth, Routledge Isobel, Linton Natalie M, Thakur Mugdha, Boehm Alexandria, Wolfe Marlene, Bischel Heather N, Naughton Colleen C, Yu Alexander T, White Lauren A, León Tomás M
California Department of Public Health Center for Infectious Diseases, 850 Marina Bay Parkway, Richmond, CA 94804, United States.
California Department of Public Health Center for Infectious Diseases, 850 Marina Bay Parkway, Richmond, CA 94804, United States.
Epidemics. 2025 Mar;50:100803. doi: 10.1016/j.epidem.2024.100803. Epub 2024 Dec 6.
The effective reproduction number serves as a metric of population-wide, time-varying disease spread. During the early years of the COVID-19 pandemic, this metric was primarily derived from case data, which has varied in quality and representativeness due to changes in testing volume, test-seeking behavior, and resource constraints. Deriving nowcasting estimates from alternative data sources such as wastewater provides complementary information that could inform future public health responses. We estimated county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 effective reproduction numbers from May 1, 2022 to April 30, 2023 for five counties in California with heterogeneous population sizes, clinical testing rates, demographics, wastewater coverage, and sampling frequencies. We used two methods to produce sewershed-restricted effective reproduction numbers, both based on smoothed and deconvolved wastewater concentrations. We then population-weighted and aggregated these sewershed-level estimates to arrive at county-level effective reproduction numbers. Using mean absolute error (MAE), Spearman's rank correlation (ρ), confusion matrix classification, and cross-correlation analyses, we compared the timing and trajectory of our two wastewater-based models to: (1) a publicly available, county-level ensemble of case-based estimates, and (2) county-aggregated, sewershed-restricted case-based estimates. Both wastewater models demonstrated high concordance with the traditional case-based estimates, as indicated by low mean absolute errors (MAE ≤ 0.09), significant positive Spearman correlation (ρ ≥ 0.66), and high confusion matrix classification accuracy (≥ 0.81). The relative timings of wastewater- and case-based estimates were less clear, with cross-correlation analyses suggesting strong associations for a wide range of temporal lags that varied by county and wastewater model type. This methodology provides a generalizable, robust, and operationalizable framework for estimating county-level wastewater-based effective reproduction numbers. Our retrospective evaluation supports the potential usage of real-time wastewater-based nowcasting as a complementary epidemiological tool for surveillance by public health agencies at the state and local levels. Based on this research, we produced publicly available wastewater-based nowcasts for the California Communicable diseases Assessment Tool (calcat.cdph.ca.gov).
有效再生数是衡量全人群中随时间变化的疾病传播情况的指标。在新冠疫情的最初几年，这一指标主要来源于病例数据，由于检测量、求诊行为和资源限制的变化，这些数据在质量和代表性方面存在差异。从废水等替代数据源得出即时预测估计值，可提供补充信息，为未来的公共卫生应对措施提供参考。我们估算了2022年5月1日至2023年4月30日期间，加利福尼亚州五个县基于县汇总、流域受限废水的新冠病毒有效再生数，这些县的人口规模、临床检测率、人口统计学特征、废水覆盖范围和采样频率各不相同。我们使用两种方法得出流域受限的有效再生数，两种方法均基于平滑和去卷积后的废水浓度。然后，我们对这些流域层面的估计值进行人口加权和汇总，得出县级有效再生数。我们使用平均绝对误差（MAE）、斯皮尔曼等级相关性（ρ）、混淆矩阵分类和互相关分析，将我们基于废水的两个模型的时间和轨迹与以下两者进行比较：（1）一个公开可用的基于病例估计值的县级综合模型，以及（2）县汇总、流域受限的基于病例的估计值。两个废水模型均与传统的基于病例的估计值高度一致，表现为平均绝对误差较低（MAE≤0.09）、斯皮尔曼正相关性显著（ρ≥0.66）以及混淆矩阵分类准确率较高（≥0.81）。基于废水和基于病例的估计值的相对时间不太明确，互相关分析表明，在广泛的时间滞后范围内存在强关联，这些滞后因县和废水模型类型而异。这种方法为估算基于县级废水的有效再生数提供了一个可推广、稳健且可操作的框架。我们的回顾性评估支持将基于实时废水的即时预测作为州和地方公共卫生机构进行监测的补充流行病学工具的潜在用途。基于这项研究，我们为加利福尼亚传染病评估工具（calcat.cdph.ca.gov）生成了公开可用的基于废水的即时预测。
