Machine learning-enabled wastewater-based surveillance for emerging pathogen detection and monitoring: current applications, challenges, and future prospects.

Abstract

AbstractWastewater-based surveillance (WBS) has become an important public-health tool for tracking community-level circulation of emerging and re-emerging pathogens, but wastewater measurements are not directly interpretable public-health indicators. Signals recovered from sewer systems are shaped by sampling variability, environmental and laboratory noise, population-dependent bias, and high-dimensional molecular complexity. Machine learning (ML) is increasingly used to extract predictive and decision-relevant information from these data, but the strength of the supporting evidence varies substantially. This narrative review examines how strongly current applications are supported by published evidence. Sources were identified through iterative searches of three literature-search sources updated to June 2026 and classified by role, analytical task, and strength of evidence. To keep the analytical scope consistent, we use a pragmatic four-layer classification: classical statistical models, conventional machine learning, deep learning, and hybrid or mechanistically informed approaches. Applications are reviewed across five analytical tasks: predictive modeling and forecasting, anomaly and fluctuation detection, data harmonization and normalization, high-dimensional interpretation, and integrated decision support. Among 35 sources reporting analytical or modeling work, 25 analyzed SARS-CoV-2 alone; demonstrated early-warning evidence came predominantly from L1 methods, and most target-task combinations remain prospective. Even within SARS-CoV-2 surveillance, reported performance was context-dependent. We examine the data-quality, methodological, and implementation barriers that limit broader application and validation. Future priorities include standardized and collaborative frameworks, multimodal data fusion, mechanistic and digital-twin modeling with explicit uncertainty assessment, and transparent and actionable analytics, with broader surveillance applications constrained by governance and public acceptability.