Wastewater-Based Epidemiology Assessment and Surveillance, 2nd Edition

A Special Issue of Environments (ISSN 2076-3298) belonging to the section "Environmental Monitoring and Management".

Deadline for manuscript submissions: 25 February 2027 | Viewed by 961

Editors


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Guest Editor
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
Interests: bioinformatics; wastewater-based epidemiology; biomarker detection
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wastewater-based epidemiology (WBE) is rapidly evolving from a tool mainly associated with COVID-19 monitoring into a broader framework for integrated public health and environmental surveillance. Recent advances in multiplex PCR, wastewater genomics, metagenomic sequencing, and data-driven modeling are making it increasingly feasible to monitor multiple pathogens and chemical biomarkers simultaneously, improve signal normalization and interpretation, and translate wastewater data into earlier and more actionable public health intelligence.

This Special Issue aims to highlight recent methodological, analytical, and translational advances in wastewater-based epidemiology and wastewater and environmental surveillance. We welcome original research and review articles addressing multi-pathogen surveillance of respiratory, enteric, and emerging pathogens; antimicrobial resistance and resistome profiling; wastewater genomics and variant tracking; biomarker-based assessment of pharmaceuticals, illicit drugs, PFAS, and other environmental contaminants; and integrative approaches that combine wastewater, clinical, demographic, and environmental data. Contributions focused on sampling strategy, quality assurance, normalization, uncertainty analysis, cross-site comparability, and machine learning for trend detection or forecasting are particularly encouraged.

We also invite submissions that examine how WBE can better support real-world public health decision making, including early warning systems, outbreak preparedness, One Health surveillance, environmental justice, and governance, ethics, and privacy considerations. Interdisciplinary contributions from environmental science, epidemiology, microbiology, analytical chemistry, bioinformatics, data science, and public policy are especially welcome.

Dr. Zhenyu Wu
Prof. Dr. Jason Levy
Guest Editors

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Keywords

  • wastewater-based epidemiology (WBE)
  • wastewater and environmental surveillance (WES)
  • multi-pathogen surveillance
  • public health surveillance
  • wastewater genomics
  • metagenomic sequencing
  • digital PCR
  • antimicrobial resistance (AMR)
  • One Health
  • chemical biomarkers
  • community exposure assessment
  • data normalization
  • epidemiological modeling
  • machine learning
  • early warning systems

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Published Papers (1 paper)

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Review

35 pages, 1394 KB  
Review
Toward Sub-Sewershed Spatio-Temporal Wastewater Surveillance: A Critical Review and a Candidate Multimodal Foundation-Model Framework
by Diego F. Cuadros, Xi Chen and Ming Tang
Environments 2026, 13(7), 382; https://doi.org/10.3390/environments13070382 - 7 Jul 2026
Viewed by 731
Abstract
Wastewater-based epidemiology (WBE) has matured into a population-level surveillance complement with operational precedent in poliovirus environmental surveillance, institutionalised systems for SARS-CoV-2, and expanding evidence across respiratory pathogens, substance-use markers, and antimicrobial-resistance targets at uneven maturity. The unresolved problem is specific: sub-sewershed spatio-temporal inference [...] Read more.
Wastewater-based epidemiology (WBE) has matured into a population-level surveillance complement with operational precedent in poliovirus environmental surveillance, institutionalised systems for SARS-CoV-2, and expanding evidence across respiratory pathogens, substance-use markers, and antimicrobial-resistance targets at uneven maturity. The unresolved problem is specific: sub-sewershed spatio-temporal inference under sewer-network and observational aggregation. Intra-catchment heterogeneity, hydraulic dynamics, and equity-relevant population differences can all be obscured by aggregate-scale modelling. Current artificial intelligence/machine learning (AI/ML) methods in WBE can be organised into four threads: temporal forecasting, spatial–statistical localisation, sewer-network and hydraulic transport, and cross-site transfer. These methods solve useful parts of the surveillance problem at the scales they target, but they do not yet supply transferable latent sub-sewershed representations under downstream aggregation. This review proposes a candidate multimodal foundation-model framework that treats place and time as jointly learnable entities, integrates a graph backbone over the sewer-network topology, incorporates physics-informed constraints, and embeds equity-conscious downstream validation as a design requirement. The framework is intended to make sub-sewershed hypotheses explicit, testable, uncertainty-bounded, and accountable to environmental-justice-relevant external validation. Whether those hypotheses survive empirical testing remains an open question. Full article
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