Methods for cost-efficient, whole genome sequencing surveillance for enhanced detection of outbreaks in a hospital setting

Abstract

Outbreaks of healthcare-associated infections (HAI) result in substantial patient morbidity and mortality; mitigation efforts by infection prevention teams have the potential to curb outbreaks and prevent transmission to additional patients. The incorporation of whole genome sequencing (WGS) surveillance of suspected high-risk pathogens often identifies outbreaks that are not detected by traditional infection prevention methods and provides evidence for transmission.

Introduction

Healthcare-associated infections (HAIs) are associated with substantial morbidity and mortality. HAIs also impose a significant economic burden on healthcare systems, costing hospitals an estimated $9.6 billion USD/year [1,2]. Whole genome sequencing (WGS) for HAI organisms can provide insight on the transmission dynamics in hospital settings [3–5]. Historically, determining the degree of genomic relatedness between organisms was accomplished using pulsed field gel electrophoresis (PFGE) [6]. Given its many advantages and recent decline in cost, WGS has emerged as the leading method for determining genetic relatedness between clinical isolates [7,8]. 

Materials and methods

Study setting

MiGEL is a non-Clinical Laboratory Improvement Amendments (CLIA) certified research laboratory located on the University of Pittsburgh main campus, in Pittsburgh PA, USA. EDS-HAT was developed and is currently implemented in real-time at MiGEL in coordination with the University of Pittsburgh, UPMC, the UPMC Clinical Laboratory Building (CLB) team, the UPMC IP&C team, and Carnegie Melon University (CMU). UPMC Presbyterian is an adult tertiary acute care hospital with 758 total beds, 134 critical care beds, and over 400 annual solid organ transplants.

Results

Weekly sequencing runs

From March 2022 to March 2025, MiGEL conducted real-time EDS-HAT, collecting and sequencing 7,850 bacterial isolates, with a weekly average sample count of N = 60 or 80 (Phases 1 or 2, respectively) and an average genome size of 4.87 million nucleotides. The average sequencing depth was 124.8× (SD, 75.3×) for Phase 1 and 281.0× (SD, 227.3×) for Phase 2. All samples collected were queued for sequencing, except for six isolates that were considered duplicates (same patient and species within 60 days), had insufficient DNA quality, or had observable discordant phenotypic morphology. 

Discussion

In this study, we detailed an efficient laboratory workflow, our approach for bioinformatics analyses, and estimated the cost associated with implementing real-time WGS surveillance for pathogenic bacteria in a hospital system. EDS-HAT began in 2016 as a retrospective study [10] and, once we demonstrated the superiority of the system over traditional approaches, transitioned in November 2021 to a real-time workflow, subsequent bioinformatic analyses, and reporting of results to the hospital IP&C team [11]. MiGEL has been performing prospective WGS surveillance for multiple organisms in real time for the UPMC hospital system for over four years. 

Acknowledgments

The authors would like to thank SeqCenter and Azenta for their assistance with sequencing. We thank the leaders and staff of the UPMC Clinical Laboratories, especially Tung Phan, MD, PhD, D(ABMM), Hannah Creager PhD, D(ABMM), and all members of the UPMC Presbyterian/Shadyside Infection Prevention & Control Team, especially Graham Snyder, MD and Ashley Ayres, MBA, CIC for their continued support. We also thank Jane Marsh, PhD, for her contributions to the EDS-HAT project.

Citation: Waggle KD, Griffith MP, Rokes AB, Rangachar Srinivasa V, Nawrocki EM, Ereifej D, et al. (2026) Methods for cost-efficient, whole genome sequencing surveillance for enhanced detection of outbreaks in a hospital setting. PLoS One 21(9): e0355550. https://doi.org/10.1371/journal.pone.0355550

Editor: Tomasz W. Kaminski, Versiti Blood Research Institute, UNITED STATES OF AMERICA

Received: December 2, 2025; Accepted: July 23, 2026; Published: September 1, 2026

Copyright: © 2026 Waggle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The whole genome sequencing data generated in this study are deposited in the United States National Institutes of Health, National Library of Medicine (https://www.ncbi.nlm.nih.gov/bioproject), and are publicly available under BioProject accession PRJNA475751. All supporting data and protocols are provided within the article or through supplementary data files.

Funding: This work was supported by the Division of Intramural Research, National Institute of Allergy and Infectious Diseases (R01AI127472 [Dr Lee H Harrison], R21AI109459 [Dr Lee H Harrison], and R21AI178369 [Dr Lee H Harrison]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: LHH and AJS serve on the scientific advisory board of Next Gen Diagnostics. LLP reports grant funding from AstraZeneca. Neither company had a role in the study design, data collection, analysis, interpretation, or writing of this manuscript. The other authors declare that there are no conflicts of interest, including financial interests, activities, relationships, and affiliations.