New K01 funds genomic surveillance development
Through 15 seasons and four spinoff shows, the intrepid investigators of television’s CSI franchise solved crimes with forensic science. Alexander Sundermann (DrPH, EPI ’22, MPH, IDM ’14) assistant professor of epidemiology, is investigating health care-associated infections with the same zeal to stop dangerous pathogens in their tracks—before they spread.
With K01 grant funding from the National Institute of Allergy and Infectious Diseases, Sundermann and colleagues will develop a methodology to improve detection and control of health care-associated outbreaks. K01 training grants provide support and protected time for an intensive, supervised career-development experience in the biomedical, behavioral, or clinical sciences leading to research independence.
With approximately $643,000 over the next five years, Sundermann’s project will explore combining real-time whole-genome sequencing surveillance with targeted environmental sampling to better detect and interrupt infection transmission in health care settings.
“A key part of this work is focused on training,” said Sundermann, who will be building skills in bacterial pathogen genomics, bioinformatics, machine learning and applied epidemiology. “The ultimate goal is to help advance genomic epidemiology as a practical, scalable infection-prevention strategy in health care,” he said.
Sundermann’s primary mentor for the project is Lee Harrison, MD, professor of infectious diseases, School of Medicine, and of epidemiology, School of Public Health.
“The project builds off the work I’ve done with Dr. Harrison on our EDS-HAT (Enhanced Detection System for Healthcare-Associated Transmission) tool, doing genomic surveillance of patient infections in the hospital,” Sundermann continued, adding that such surveillance isn’t done in other hospitals. “With EDS-HAT, we find outbreaks that would otherwise go completely undetected, and we also know that by doing genomic surveillance of patient infections, we can stop these outbreaks from progressing.”
Typical infection-control interventions like frequent handwashing and thorough cleaning of high-touch areas and medical equipment are included among project parameters. But the combination of genomic surveillance and targeted environmental sampling should provide information to identify potentially dangerous infectious agents more accurately and intervene before transmission can take place.
“Say there’s an outbreak in the medical ICU, but we don’t know exactly how it happened. We start sampling areas or equipment that we suspect are involved, recover bacteria and do genomic sequencing to see whether it matches,” he said. “With those data, we can know exactly where the bacteria are hiding and what we have to do to stop the outbreak.”
The second project aim is to evaluate the amassed data to develop risk-prediction models to identify hazardous transmission scenarios to help prevent infections rather than react once they appear. “We want to be proactive rather than reactive,” said Sundermann.
Advisory committee members include Maria Brooks, PhD, professor of epidemiology, School of Public Health; Harry Hochhesier, PhD, associate professor of biomedical informatics, School of Medicine; and Artur Dubrawski, PhD, MS, a leading expert in machine learning at Carnegie Mellon University.
-- Michele Dula Baum