Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospitals using genomic, microbiological and patient mobility data

Sen Pei (), Dwayne Seeram, Seth Blumberg, Bo Shopsin, Anne-Catrin Uhlemann and Jeffrey Shaman
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Sen Pei: Columbia University, Department of Environmental Health Sciences, Mailman School of Public Health
Dwayne Seeram: Columbia University, College of Physicians and Surgeons, Division of Infectious Diseases, Department of Medicine
Seth Blumberg: University of California, F.I. Proctor Foundation
Bo Shopsin: New York University Grossman School of Medicine, Department of Microbiology
Anne-Catrin Uhlemann: Columbia University, College of Physicians and Surgeons, Division of Infectious Diseases, Department of Medicine
Jeffrey Shaman: Columbia University, Department of Environmental Health Sciences, Mailman School of Public Health

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Asymptomatic carriers of antimicrobial-resistant organisms (AMROs) can unwittingly transmit these pathogens in hospitals, contributing to the burden of healthcare-associated infections (HAIs). Surveillance in hospitals can involve different types of observations; however, a framework to coherently synthesize these datasets to identify AMRO carriers is lacking. Here, we develop a new inference framework combining a data-driven mechanistic transmission model and multimodal observations from clinical cultures, electronic health records, patient mobility, and genomic data. Using extensive simulated outbreaks, we validate the inference framework for AMROs with various levels of community importation and hospital transmission and evaluate the utility of different combinations of data sources. Inference results show that using multimodal observations consistently improves the accuracy in identifying AMRO carriers. We apply the inference framework to carbapenem-resistant Klebsiella pneumoniae (CRKP) at an urban quaternary care hospital in New York City, United States and find that the addition of even sparsely sampled genome sequence data to patient characteristics supports more accurate identification of CRKP carriers. Model simulations suggest that inference-guided targeted isolation leads to a greater reduction of AMRO burdens compared to alternative, heuristic approaches. Thus, the synergistic effect of utilizing multimodal observations for estimating AMRO carriage risk may inform improved interventions in hospital settings.

Date: 2025
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DOI: 10.1038/s41467-025-65241-w

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