Genomic Surveillance and Epidemiological Modeling of Antimicrobial Resistance (AMR) in Urban Hospital EffluentsA Methodological Framework Integrating Metagenomic Surveillance with Dynamic Transmission Modeling of the Hospital–Effluent–Community Continuum

Abstract

Antimicrobial resistance (AMR) is among the most pressing threats to global public health, and urban hospitals are increasingly recognized as concentrated point sources of resistant organisms and resistance genes discharged into municipal sewer systems. This paper presents an integrated framework that combines genomic (metagenomic) surveillance of hospital effluent with dynamic epidemiological modeling to characterize the emergence, persistence, and downstream dissemination of AMR determinants from urban healthcare facilities. We synthesize current evidence on the resistome of hospital wastewater, describe a reproducible sampling and shotgun-metagenomic sequencing workflow spanning ward-level drains, wastewater treatment plant (WWTP) influent and effluent, and downstream receiving water, and couple this with a compartmental transmission model linking in-hospital colonization dynamics to an environmental reservoir compartment representing the effluent system. Using illustrative simulation informed by patterns reported in the literature, we show that the resistant-organism load discharged into effluent responds non-linearly to both clinical antimicrobial stewardship and effluent treatment intensification, and that combined interventions produce substantially larger reductions in downstream environmental resistance load than either intervention alone. We further present a simulated but literature-consistent resistome profile showing that beta-lactamase and aminoglycoside resistance genes dominate hospital-associated effluent and are only partially attenuated by conventional wastewater treatment. We discuss the implications of these findings for One Health AMR surveillance architecture, the technical and governance barriers to operationalizing genomic wastewater-based epidemiology (WBE) at the hospital scale, and priority directions for coupling sequencing data streams with mechanistic and statistical models to generate actionable, near-real-time risk indicators. The framework is intended as a template that can be parameterized with site-specific sequencing and clinical data to support hospital infection control, environmental regulation, and regional AMR early-warning systems.