Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties.

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

Abstract

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
Infectious diseases: Household modeling with missing data. Transmission models of respiratory infections in carceral settings: A systematic review. Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties. Building in-house capabilities in health agencies and outsourcing to academia or industry: Considerations for effective infectious disease modelling. Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach.
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