Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism

Infect Control Hosp Epidemiol. 2023 Feb;44(2):246-252. doi: 10.1017/ice.2022.66. Epub 2022 Sep 16.


Objective: From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation.

Methods: We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach.

Results: We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machine-learning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms.

Conclusions: We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Bacterial Agents / therapeutic use
  • Child
  • Cross Infection* / drug therapy
  • Cross Infection* / epidemiology
  • Cross Infection* / prevention & control
  • Disease Outbreaks
  • Drug Resistance, Multiple, Bacterial
  • Gram-Positive Bacterial Infections* / drug therapy
  • Gram-Positive Bacterial Infections* / epidemiology
  • Hospitals
  • Humans
  • Risk Factors
  • Vancomycin-Resistant Enterococci*


  • Anti-Bacterial Agents