Real-Time Investigation of a Large Nosocomial Influenza A Outbreak Informed by Genomic Epidemiology

Background. Nosocomial respiratory virus outbreaks represent serious public health challenges. Rapid and precise identification of cases and tracing of transmission chains is critical to end outbreaks and to inform prevention measures. Methods. We combined conventional surveillance with influenza A virus (IAV) genome sequencing to identify and contain a large IAV outbreak in a metropolitan healthcare system. A total of 381 individuals, including 91 inpatients and 290 healthcare workers (HCWs), were included in the investigation. Results. During a 12-day period in early 2019, infection preventionists identified 89 HCWs and 18 inpatients as cases of influenza-like illness (ILI), using an amended definition without the requirement for fever. Sequencing of IAV genomes from available nasopharyngeal specimens identified 66 individuals infected with a nearly identical strain of influenza A H1N1pdm09 (43 HCWs, 17 inpatients, and 6 with unspecified affiliation). All HCWs infected with the outbreak strain had received the seasonal influenza virus vaccination. Characterization of 5 representative outbreak viral isolates did not show antigenic drift. In conjunction with IAV genome sequencing, mining of electronic records pinpointed the origin of the outbreak as a single patient and a few interactions in the emergency department that occurred 1 day prior to the index ILI cluster. Conclusions. We used precision surveillance to delineate a large nosocomial IAV outbreak, mapping the source of the outbreak to a single patient rather than HCWs as initially assumed based on conventional epidemiology. These findings have important ramifications for more-effective prevention strategies to curb nosocomial respiratory virus outbreaks.

Automated summary

In early 2019, a large nosocomial influenza A virus outbreak was traced back to a single patient rather than healthcare workers, as initially assumed based on conventional epidemiology, using a combination of conventional surveillance with IAV genome sequencing and mining of electronic records.