Integration of genomic and clinical data augments surveillance of healthcare-acquired infections

UMMS Affiliation

Center for Microbiome Research; Information Technology Data Sciences and Technology; Department of Quantitative Health Sciences; Department of Microbiology and Physiological Systems; Infection Control Department, UMass Memorial Medical Center

Publication Date


Document Type



Bacterial Infections and Mycoses | Clinical Epidemiology | Computational Biology | Epidemiology | Health Information Technology | Health Services Administration | Health Services Research | Infectious Disease | Pathogenic Microbiology


BACKGROUND: Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.

OBJECTIVE: To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.

METHODS: Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.

RESULTS: Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.

CONCLUSIONS: Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.

DOI of Published Version



Infect Control Hosp Epidemiol. 2019 Jun;40(6):649-655. doi: 10.1017/ice.2019.75. Epub 2019 Apr 23. Link to article on publisher's site

Journal/Book/Conference Title

Infection control and hospital epidemiology

PubMed ID



Full author list omitted for brevity. For the full list of authors, see article.

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Link to Article in PubMed