Using Genomic Epidemiology to Track Respiratory Virus Transmission in Hospitals

Using Genomic Epidemiology to Track Respiratory Virus Transmission in Hospitals

Vatsala Rangachar Srinivasa, MPH, a PhD candidate in epidemiology at the University of Pittsburgh, is focusing her research on genomic epidemiology of respiratory viruses—work she will present at the Society for Healthcare Epidemiology of America (SHEA) Spring Conference from April 27 to 30, 2025, in ChampionsGate, Florida.

As part of the Microbial Genomic Epidemiology Laboratory under Lee Harrison, MD, she contributes to the National Institutes of Health-funded Enhanced Detection System for Health care-Associated Transmission (EDS-HAT). While the team initially focused on bacterial infections, Srinivasa has expanded the scope to include seasonal respiratory viruses, exploring the potential of genomic surveillance in identifying health care-associated transmissions.

“As part of this project, we have established a successful genomic surveillance program to track the spread of bacteria in our hospital, and we are currently working on expanding this to common seasonal respiratory viruses,” Srinivasa told Infection Control Today^®. “And we began this work just before the COVID-19 pandemic hit in December 2019. Currently, sequencing respiratory viruses is not standard practice in hospitals for monitoring spread, so we started this project to explore the utility and benefits of routine genomic surveillance of respiratory viruses.”

Her study involved sequencing 4 common respiratory viruses—rhinovirus, influenza, RSV, and human metapneumovirus—across 3 hospitals, 2 serving adults and 1 pediatric. The retrospective analysis included data from 2017 to 2019 and focused on patients who tested positive for these viruses after at least 3 days in the hospital to distinguish likely hospital-acquired infections. Out of approximately 300 sequenced specimens, around 10% were closely genetically related, and 53% of these cases had identifiable hospital-based epidemiological links, such as shared units, overlapping stays, or common health care providers. The remaining 47% lacked identifiable links, suggesting potential transmission from undetected sources such as visitors, health care staff, or the broader community.

While the findings demonstrated clear clusters and transmission patterns, the study's retrospective nature presented limitations. Since the sequencing occurred years after the infections, there was no opportunity to investigate staff illness or real-time interactions that might have explained specific transmission chains. The data collection also preceded the COVID-19 pandemic, when masking and infection control protocols were less stringent, potentially allowing more unmonitored spread.

“Our study focused only on sequencing HAIs, that is, when a person tested positive for respiratory virus on or after 3 days of staying at a hospital. We might not have captured the full scope of transmission. For instance, we did not track or sequence cases, if a patient tested positive on their first or the second day of hospital, [for example], and we also had a few cases where samples from different hospitals were clustering together, or they were closely genetically related, which I found was very interesting, but there was no way for us to see if there was any epidemiological links between them. We have also not explored how infections acquired in the community would play a role in the transmission of these viruses. So what we have found could be an underestimation of transmission.”

Unexpectedly, some genetically linked viruses were found across different hospitals, which presented further questions about the transmission sources and raised the possibility of wider regional or community-based spread. However, the study only looked at infections diagnosed 3 days into a hospital stay or later, potentially underestimating the full scope of transmission, as patients infected earlier were omitted.

Srinivasa emphasized that whole genome sequencing for viral surveillance is not standard in most hospitals, particularly outside large or research-focused institutions. Infection prevention teams often lack access to this level of genomic data, meaning many potential transmission events go unnoticed. Her presentation aimed to showcase how routine genomic surveillance could fill this gap, improving early detection and helping refine infection prevention strategies.

One of the major barriers is that viral sequencing is more complex and costly than bacterial sequencing. Respiratory virus samples are not pure cultures but mixtures of various organisms, requiring additional lab steps to isolate and enrich the virus of interest. These methods are time-consuming and labor-intensive, often taking days to complete. Despite these challenges, Srinivasa’s team has refined its protocols and successfully implemented them in a laboratory setting.

“Implementing this technology presents numerous challenges, especially with viral sequencing, which is more complex than bacterial sequencing,” Srinivasa said. “It tends to be more expensive, labor-intensive, and takes significantly longer to generate results than bacterial sequencing. When we initiated this project in 2019, we spent a lot of time in the lab, optimizing all protocols and refining the genomic analysis methods to ensure effectiveness and achieve project goals."

Currently, "as we are in the final stages of drafting our manuscript, we anticipate submitting it to a journal for peer review very soon, and we look forward to sharing the results with everyone [soon]. Our next step is to validate these findings further and see the best ways to utilize genomic surveillance for respiratory viruses in hospitals.”