Is Wastewater Surveillance Predictive during the Endemic Phase of Respiratory Disease? An Analysis Based on Clinical, Wastewater, and Digital Search Data in Detroit, Michigan

废水 流行病学 环境卫生 鉴定(生物学) 环境科学 污水处理 缺少数据 疾病监测 预测值 公共卫生 环境工程 传输(电信) 地理 呼吸系统 流行病学监测 案件调查 数字健康 2019年冠状病毒病(COVID-19)
作者
Liang Zhao,Niloofar Gohari,Reegan Kelly,Pankaj Bhatt,Mike Swain,Russell A. Faust,John Norton,Irene Xagoraraki
出处
期刊:Journal of Environmental Engineering [American Society of Civil Engineers]
卷期号:152 (12)
标识
DOI:10.1061/joeedu.eeeng-8872
摘要

Abstract Wastewater-based epidemiology (WBE) has emerged as a major public health innovation catalyzed by the COVID-19 pandemic. However, its potential remains underexplored during and after COVID-19’s transition to endemicity. This study evaluates WBE’s potential to identify the COVID-19 pandemic-to-endemic transition and to provide early warnings across multiple surveillance systems for influenza A (IAV) and B (IBV), respiratory syncytial virus (RSV), and SARS-CoV-2 in the postpandemic era. We monitored SARS-CoV-2 N1 concentrations in wastewater from April 8, 2020, and July 31, 2025, generating the earliest and longest-running wastewater dataset in Detroit, MI. A peak identification method was implemented to identify the pandemic-to-endemic transition by comparing the frequency of N1 concentration peaks between phases. During the endemic phase, from October 1, 2022, and March 31, 2025, we monitored RSV, IAV, IBV, and SARS-CoV-2 in Detroit’s wastewater. Pearson correlations were implemented to quantify the associations between wastewater concentrations and clinical, syndromic, and digital epidemiological data. Time-lagged cross-correlation (TLCC) was used to examine temporal dynamics among these datasets and identify the earliest emerging data for each disease and time lags. Extensive literature studies were conducted to elucidate the time-lag mechanisms for each disease, embracing the TLCC results. Random forest models were established to predict clinical cases based on WBE datasets. This was among the first studies using WBE-based approaches to identify pandemic-to-endemic transition of SARS-CoV-2. The relationships of wastewater data to traditional clinical, syndromic, and digital epidemiological surveillance data were systematically analyzed during the endemic phase of these respiratory diseases. This study demonstrates the predictive value of WBE during the endemic phase, providing early warnings and predictions for seasonal respiratory diseases, including COVID-19, influenza, and flu-like disease.

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