WASTEWATER-BASED EPIDEMIOLOGY: ANALYTICAL METHODS FOR TRACKING COMMUNITY DRUG USE AND ANTIMICROBIAL RESISTANCE

Background: Wastewater-based epidemiology (WBE) has evolved from a niche forensic tool into an essential, population-scale pillar of modern public health intelligence, offering non-invasive and objective insights into community health dynamics. Objectives: This review provides a comprehensive synthesis of contemporary literature on WBE, explicitly focusing on advanced analytical workflows for tracking community drug use, unintentional chemical exposures, epidemic viral pathogens, and antimicrobial resistance (AMR). Methods: Following a systematic bibliographic search across major scientific repositories (PubMed/Medline, Scopus, Web of Science, and Engineering Village), we evaluated recent advances in sample collection, pre-treatment, concentration, and quantification. Examined analytical platforms include liquid chromatography-tandem mass spectrometry (LC-MS/MS), environmental proteomics, reverse transcription-quantitative PCR (RT-qPCR), droplet digital PCR (ddPCR), next-generation sequencing (NGS) metaviromics, and field-deployable biosensors. Results: Findings demonstrate that rigorous control over pre-analytical variables—such as in-sewer transformation kinetics, biofilm degradation, and biomarker stability—is vital for accurate per capita back-calculation. Wastewater surveillance successfully circumvents the reactive bottlenecks of traditional clinical reporting, providing robust early-warning lead times ranging from 4 to 17 days for viral resurgences like influenza and SARS-CoV-2. Furthermore, urban sewer networks and hospital wastewater influents act as critical ecological hotspots, concentrating clinically vital resistance determinants (sul1/2, ermB/F, qnrS, mcr variants, and beta-lactamase genes) driven by therapeutic selection pressures and horizontal gene transfer. Conclusions: The convergence of advanced molecular detection, environmental proteomics, point-of-need biosensors, and artificial intelligence-driven predictive modeling is transforming WBE into a proactive early-warning architecture. Institutionalizing WBE within a unified One Health framework remains vital for safeguarding global health security and preempting future public health crises.