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I study hospital outbreaks using pathogen genomics and machine learning of electronic health record data


Dr. Alexander Sundermann is an Assistant Professor at the University of Pittsburgh, Division of Infectious Diseases, Center for Genomic Epidemiology. His work focuses on the research and application of whole genome sequencing surveillance for outbreak detection and investigation in healthcare settings. Dr. Sundermann’s research also utilizes the electronic health record data and machine learning to detect routes of transmission faster and more accurately within outbreaks. More recently, he and the Center for Genomic Epidemiology implemented a real-time whole genome sequencing surveillance program at their hospital which sequences potentially healthcare-associated bacterial infections every week to monitor for transmission, outbreaks, and direct interventions.

Dr. Sundermann previously worked as a Senior Infection Preventionist at the University of Pittsburgh Medical Center where he led investigations into multiple outbreaks and directed patient safety and quality improvement initiatives. He is a Fellow of the Association for Professionals in Infection Control and Epidemiology and board certified in infection control by the Certification Board of Infection Control and Epidemiology.


( 01 )


March 2022

Doctor of Public Health
University of Pittsburgh

Dissertation title: “Novel Approaches for Healthcare Outbreak Detection and Investigation"

December 2014

Master of Public Health
Infectious Diseases and Microbiology
University of Pittsburgh

Thesis title: “Development of Protocol for Reduction in Central Line Associated Blood Stream Infections”

May 2013

Bachelor of Science
University of Rochester


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July 2022 - Present

Assistant Professor of Infectious Diseases, University of Pittsburgh

Further developing the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) for hospital outbreak detection and investigation

August 2020 - June 2022

Epidemiologist, University of Pittsburgh

Leading research into the development of Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) which combines whole genome sequencing surveillance and machine learning of the electronic health record for healthcare outbreak detection and investigation

November 2019 - July 2020

Epidemiologist, Cardno ChemRisk

Provided epidemiological support and analysis in literature review, health policy, microbial assessments, data analysis, data management, and litigation

December 2014 - November 2019

Senior Infection Preventionist, UPMC

Provided surveillance and epidemiological insight and implement interventions to reduce healthcare-associated infections


( 03 )

Recent Publications

June 2022

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

November 2021

Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection

April 2022

Remediation of Mucorales-contaminated Healthcare Linens at a Laundry Facility Following an Investigation of a Case Cluster of Hospital-acquired Mucormycosis


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Recent Media

The Conversation, Jan 2022

From delta to omicron, here’s how scientists know which coronavirus variants are circulating in the US

Pittsburgh Post Gazette, Nov 2021

Artificial intelligence system may help fight spread of infections in hospitals, new Pitt and CMU study reports

Wall Street Journal, Nov 2021

Pittsburgh Hospital Taps AI to Prevent Spread of Infections

The Conversation, Mar 2021

Genomic surveillance: What it is and why we need more of it to track coronavirus variants and help end the COVID-19 pandemic

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