利用加州各县多个下水道的污水数据估计SARS-CoV-2的有效繁殖数量。

IF 3 3区 医学 Q2 INFECTIOUS DISEASES Epidemics Pub Date : 2024-12-06 DOI:10.1016/j.epidem.2024.100803
Sindhu Ravuri, Elisabeth Burnor, Isobel Routledge, Natalie M Linton, Mugdha Thakur, Alexandria Boehm, Marlene Wolfe, Heather N Bischel, Colleen C Naughton, Alexander T Yu, Lauren A White, Tomás M León
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引用次数: 0

摘要

有效繁殖数可作为种群范围内随时间变化的疾病传播的度量。在COVID-19大流行的最初几年,这一指标主要来自病例数据,由于检测量、寻求检测行为和资源限制的变化,这些数据的质量和代表性各不相同。从废水等替代数据来源得出临近预报估计数,可提供补充信息,为今后的公共卫生对策提供信息。我们估计了2022年5月1日至2023年4月30日期间加州5个县的县聚集性、下水道限制废水的SARS-CoV-2有效繁殖数,这些县的人口规模、临床检测率、人口统计学、废水覆盖率和采样频率各不相同。我们使用了两种方法来产生下水道限制的有效繁殖数,这两种方法都是基于平滑和反卷积的废水浓度。然后,我们对这些下水道水平的估计进行人口加权和汇总,以得出县级的有效再生产数字。利用平均绝对误差(MAE)、斯皮尔曼秩相关(ρ)、混淆矩阵分类和交叉相关分析,我们将两个基于废水的模型的时间和轨迹进行了比较:(1)公开可用的、县级的基于病例的估计集合,以及(2)县级汇总的、受下水道限制的基于病例的估计。这两种废水模型都与传统的基于案例的估计具有很高的一致性,这表明平均绝对误差低(MAE≤0.09),Spearman正相关显著(ρ≥0.66),混淆矩阵分类精度高(≥0.81)。基于废水和基于案例的估算的相对时间不太清楚,相互关联分析表明,随着国家和废水模型类型的不同,时间滞后的范围也有所不同。该方法为估计县级废水有效再生产数提供了一个可推广、可靠和可操作的框架。我们的回顾性评估支持将基于废水的实时临近预报作为州和地方各级公共卫生机构监测的补充流行病学工具的潜力。基于这项研究,我们为加州传染病评估工具(calcat.cdph.ca.gov)制作了公开可用的基于废水的临近预报。
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Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties.

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).

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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
期刊最新文献
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