---
title: "Wastewater-based fever prevalence indicator for early warning of acute respiratory infections usin"
id: "pubmed-42476399"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42476399"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42476399/"
doi: "10.1016/j.envres.2026.125279"
published_at: "2026-09-15T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Wastewater-based fever prevalence indicator for early warning of acute respiratory infections usin
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42476399
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42476399/)
- **DOI:** [10.1016/j.envres.2026.125279](https://doi.org/10.1016%2Fj.envres.2026.125279)
- **Published At:** 2026-09-15T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The study develops an integrated framework combining **wastewater-based epidemiology (WBE)** with **machine learning** to create a fever prevalence–based early warning model for acute respiratory infections (ARIs). - Wastewater measurements of **paracetamol** and **ibuprofen** consumption (n = 603 samples) were collected from multiple Chinese cities including Dalian, Beijing, and Guangzhou. - Eleven statistical, unsupervised, and supervised learning approaches were evaluated for anomaly detection across the wastewater-derived indicator. - Among evaluated models, the **interquartile range**, **isolation forest**, **neural networks**, and **logistic regression** showed favorable performance based on model validation and evaluation reported by the authors. - Only warning thresholds from the interquartile range (3.3%) and isolation forest (3.5%) methods classified November 2021 as non-epidemic and November 2024 as epidemic, matching influenza-like illness surveillance from sentinel hospitals. - The authors conclude that combining **WBE** with **machine learning** can deliver timely, community-level monitoring of **fever prevalence**, provide early warning for fever-dominant respiratory infections and unknown etiologies (e.g., “Disease X”), and address limitations of conventional surveillance such as delayed response and incomplete coverage. - The dataset, modeling approaches, validation outcomes, and city-level sampling are described in the abstract; additional methodological details were not reported in the abstract. - The authors declared no competing interests.
## Clinical Analysis & Structured Key Points
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Epub 2026 Jul 20. # A novel fever prevalence indicator for early warning of acute respiratory infections using wastewater-based epidemiology and machine learning [Xue-Ting Shao](https://pubmed.ncbi.nlm.nih.gov/?term=Shao+XT&cauthor_id=42476399)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#full-view-affiliation-1 "School of Marine Sciences \(State Key Laboratory of Marine Resources Utilization in South China Sea\), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xuetingshao@hainanu.edu.cn."), [Hao-Yang Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+HY&cauthor_id=42476399)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#full-view-affiliation-2 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: lhy884516914@outlook.com."), [Jing-Bo Guan](https://pubmed.ncbi.nlm.nih.gov/?term=Guan+JB&cauthor_id=42476399)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#full-view-affiliation-3 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: 1740815478gjb@dlmu.edu.cn."), [Xin-Dong Ma](https://pubmed.ncbi.nlm.nih.gov/?term=Ma+XD&cauthor_id=42476399)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#full-view-affiliation-4 "School of Marine Sciences \(State Key Laboratory of Marine Resources Utilization in South China Sea\), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xdma@hainanu.edu.cn."), [De-Gao Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+DG&cauthor_id=42476399)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#full-view-affiliation-5 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: degaowang@dlmu.edu.cn.") Affiliations Expand ### Affiliations * 1 School of Marine Sciences (State Key Laboratory of Marine Resources Utilization in South China Sea), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xuetingshao@hainanu.edu.cn. * 2 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: lhy884516914@outlook.com. * 3 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: 1740815478gjb@dlmu.edu.cn. * 4 School of Marine Sciences (State Key Laboratory of Marine Resources Utilization in South China Sea), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xdma@hainanu.edu.cn. * 5 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: degaowang@dlmu.edu.cn. * PMID: **42476399** * DOI: [ 10.1016/j.envres.2026.125279 ](https://doi.org/10.1016/j.envres.2026.125279) Item in Clipboard # A novel fever prevalence indicator for early warning of acute respiratory infections using wastewater-based epidemiology and machine learning Xue-Ting Shao et al. Environ Res. 2026. Show details Display options Display options Format Abstract PubMed PMID Environ Res Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Environ+Res%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Environ+Res%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42476399/) . 2026 Sep 15;306(Pt 3):125279. doi: 10.1016/j.envres.2026.125279. Epub 2026 Jul 20. ### Authors [Xue-Ting Shao](https://pubmed.ncbi.nlm.nih.gov/?term=Shao+XT&cauthor_id=42476399)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#short-view-affiliation-1 "School of Marine Sciences \(State Key Laboratory of Marine Resources Utilization in South China Sea\), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xuetingshao@hainanu.edu.cn."), [Hao-Yang Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+HY&cauthor_id=42476399)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#short-view-affiliation-2 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: lhy884516914@outlook.com."), [Jing-Bo Guan](https://pubmed.ncbi.nlm.nih.gov/?term=Guan+JB&cauthor_id=42476399)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#short-view-affiliation-3 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: 1740815478gjb@dlmu.edu.cn."), [Xin-Dong Ma](https://pubmed.ncbi.nlm.nih.gov/?term=Ma+XD&cauthor_id=42476399)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#short-view-affiliation-4 "School of Marine Sciences \(State Key Laboratory of Marine Resources Utilization in South China Sea\), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xdma@hainanu.edu.cn."), [De-Gao Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+DG&cauthor_id=42476399)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42476399/#short-view-affiliation-5 "College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: degaowang@dlmu.edu.cn.") ### Affiliations * 1 School of Marine Sciences (State Key Laboratory of Marine Resources Utilization in South China Sea), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xuetingshao@hainanu.edu.cn. * 2 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: lhy884516914@outlook.com. * 3 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: 1740815478gjb@dlmu.edu.cn. * 4 School of Marine Sciences (State Key Laboratory of Marine Resources Utilization in South China Sea), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, China. Electronic address: xdma@hainanu.edu.cn. * 5 College of Environmental Science and Engineering, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China. Electronic address: degaowang@dlmu.edu.cn. * PMID: **42476399** * DOI: [ 10.1016/j.envres.2026.125279 ](https://doi.org/10.1016/j.envres.2026.125279) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract The rapid spread of acute respiratory infections (ARIs) poses a major challenge to global public health, yet current surveillance systems relying on sentinel hospitals and laboratory testing often lack timeliness and precision. In this study, we present an integrated framework combining wastewater-based epidemiology (WBE) with machine learning to establish a fever prevalence-based early warning model for ARIs. Using wastewater data on paracetamol and ibuprofen consumption (n = 603) from Dalian, Beijing, Guangzhou, and other cities, we evaluated statistical, unsupervised, and supervised learning approaches (11 models) for anomaly detection. Based on model validation and evaluation, the interquartile range, isolation forest, neural networks, and logistic regression models demonstrated favorable performance. However, only the warning thresholds derived from the interquartile range method (3.3%) and isolation forest (3.5%) indicated that November 2021 was a non-epidemic period and November 2024 was an epidemic period, which is consistent with the influenza-like illness surveillance results from sentinel hospitals. Our findings demonstrate that WBE, when coupled with machine learning, can provide timely and precise community-level monitoring of fever prevalence, while also offering early-warning capability for fever-dominant emerging respiratory infections and previously unidentified etiological agents such as "Disease X". This approach effectively addresses the limitations of conventional surveillance, such as incomplete coverage and delayed response, and provides a scalable, evidence-based tool to strengthen public health preparedness and response. **Keywords:** Acute respiratory infections; Fever prevalence; Machine learning; Wastewater-based epidemiology. Copyright © 2026 Elsevier Inc. All rights reserved. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declaration of competing interest The authors declare no competing interests. ## Similar articles * [ A Systematic Review of Gastrointestinal and Respiratory Pathogen Detection in Wastewater in Africa, with Focus on Rwanda: Implications for Early Warning and Public Health Surveillance. ](https://pubmed.ncbi.nlm.nih.gov/42347188/) Bambara S, Isingizwe MC, Adegboyega TT, Mutesa L.Bambara S, et al.Pathogens. 2026 May 27;15(6):574. doi: 10.3390/pathogens15060574.Pathogens. 2026.PMID: 42347188Free PMC article. * [ Standardization of case definition and development of early-warning model for acute respiratory infection syndromes based on Yinzhou Regional Health Information Platform. ](https://pubmed.ncbi.nlm.nih.gov/40438071/) Yi T, Zhang J, Shen P, Sun Y.Yi T, et al.Front Public Health. 2025 May 14;13:1593102. doi: 10.3389/fpubh.2025.1593102. eCollection 2025.Front Public Health. 2025.PMID: 40438071Free PMC article. * [ Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study. ](https://pubmed.ncbi.nlm.nih.gov/40769217/) Cui J, Zhang T, Shen Y, Wang X, Yang L, Huang X, Huang Q, Yang Y, Yang W, Li Z.Cui J, et al.JMIR Med Inform. 2025 Aug 6;13:e73631. doi: 10.2196/73631.JMIR Med Inform. 2025.PMID: 40769217Free PMC article. * [ A review of AI/ML approaches in wastewater surveillance advancement. ](https://pubmed.ncbi.nlm.nih.gov/41576591/) Ali M, Younis AB, Duru CI, Sherchan SP.Ali M, et al.Sci Total Environ. 2026 Feb 10;1015:181364. doi: 10.1016/j.scitotenv.2026.181364. Epub 2026 Jan 22.Sci Total Environ. 2026.PMID: 41576591Review. * [ Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. ](https://pubmed.ncbi.nlm.nih.gov/34525746/) Abdeldayem OM, Dabbish AM, Habashy MM, Mostafa MK, Elhefnawy M, Amin L, Al-Sakkari EG, Ragab A, Rene ER.Abdeldayem OM, et al.Sci Total Environ. 2022 Jan 10;803:149834. doi: 10.1016/j.scitotenv.2021.149834. Epub 2021 Aug 21.Sci Total Environ. 2022.PMID: 34525746Free PMC article.Review. 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