Epidemiological and Genomic analysis of a Sydney Hospital COVID-19 Outbreak

Abstract

Early COVID-19 experience in Australia involved clusters in northern Sydney, including hospital and aged-care facility (ACF) outbreaks. We explore transmission dynamics, drivers and outcomes of a metropolitan hospital COVID-19 outbreak that occurred in the context of established local community transmission. A retrospective cohort analysis is presented, with integration of viral genome sequencing, clinical and epidemiological data. We demonstrate using genomic epidemiology that the hospital outbreak (n=23) was linked to a concurrent outbreak at a local aged care facility, but was phylogenetically distinct from other community clusters. Thirty day survival was 50% for hospitalised patients (an elderly cohort with significant comorbidities) and 100% for staff. Staff who acquired infection were unable to attend work for a median of 26.5 days (range 14-191); an additional 140 staff were furloughed for quarantine. Transmission from index cases showed a wide dispersion (mean 3.5 persons infected for every patient case and 0.6 persons infected for every staff case). One patient, who received regular nebulised medication prior to their diagnosis being known, acted as an apparent superspreader. No secondary transmissions occurred from isolated cases or contacts who were quarantined prior to becoming infectious. This analysis elaborates the wide-ranging impacts on patients and staff of nosocomial COVID-19 transmission and highlights the utility of genomic analysis as an adjunct to traditional epidemiological investigations. Delayed case recognition resulted in nosocomial transmission but once recognised, prompt action by the outbreak management team and isolation with contact and droplet (without airborne) precautions were sufficient to prevent transmission within this cohort. Our findings support current PPE recommendations in Australia but demonstrate the risk of administering nebulised medications when COVID-19 is circulating locally.

Factors contributing to a coronavirus disease 2019 (COVID-19) outbreak on a mixed medical-surgical unit in a Canadian acute-care hospital

“…This COVID-19 outbreak has similarities to other hospital-associated outbreaks. [6][7][8][9][10]20 Factors that contributed to the outbreak included failure to identify SARS-CoV-2 in admitted patients during the initial stages of infection, a largely incomplete or unvaccinated population, and patient behavioral factors such as a lack of compliance with physical distancing and mask wearing, with some patients wandering on the hospital unit. Roommates of COVID-19 patients were shown to be at a significantly higher risk of acquiring SARS-CoV-2.…”

“…In this study, we have shown the benefits of the outbreak investigation of adding WGS analysis. Other reports have highlighted the utility of WGS in outbreak investigation, 3,5,9,12,13,16,18,20,21 including its ability to identify complex outbreaks characterized by introduction of infections by >1 source or refute the original transmission hypothesis. WGS supported this study's hypothesis of the introduction of SARS-CoV-2 onto the unit by 2 patients who presented to the hospital with respiratory symptoms, one of whom was later found to have had community contact with COVID-19.…”

“…Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, 1 there have been reports of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission to patients and healthcare workers (HCW) within healthcare facilities. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] Factors contributing to these outbreaks have included patient accommodation in multibed rooms or bays, [6][7][8]11,12,17 lack of infection prevention and control precautions due to failure to identify patients admitted with or incubating SARS-CoV-2, [6][7][8][9][10]20 and nosocomial exposure to a patient with COVID-19. [15][16][17][18][19][20] Rhee et al 22 demonstrated that consistent application of infection prevention and control measures can reduce or prevent the spread of SARS-CoV-2 in healthcare settings.…”