Combined epidemiological and genomic analysis of nosocomial SARS-CoV-2 infection early in the pandemic and the role of unidentified cases in transmission

Abstract

Objectives: To analyse nosocomial transmission in the early stages of the coronavirus 2019 (COVID-19) pandemic at a large multisite healthcare institution. Nosocomial incidence is linked with infection control interventions.

Methods: Viral genome sequence and epidemiological data were analysed for 574 consecutive patients, including 86 nosocomial cases, with a positive PCR test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first 19 days of the pandemic.

Results: Forty-four putative transmission clusters were found through epidemiological analysis; these included 234 cases and all 86 nosocomial cases. SARS-CoV-2 genome sequences were obtained from 168/234 (72%) of these cases in epidemiological clusters, including 77/86 nosocomial cases (90%). Only 75/168 (45%) of epidemiologically linked, sequenced cases were not refuted by applying genomic data, creating 14 final clusters accounting for 59/77 sequenced nosocomial cases (77%). Viral haplotypes from these clusters were enriched 1-14x (median 4x) compared to the community. Three factors implicated unidentified cases in transmission: (a) community-onset or indeterminate cases were absent in 7/14 clusters (50%), (b) four clusters (29%) had additional evidence of cryptic transmission, and (c) in three clusters (21%) diagnosis of the earliest case was delayed, which may have facilitated transmission. Nosocomial cases decreased to low levels (0-2 per day) despite continuing high numbers of admissions of community-onset SARS-CoV-2 cases (40-50 per day) and before the impact of introducing universal face masks and banning hospital visitors.

Conclusion: Genomics was necessary to accurately resolve transmission clusters. Our data support unidentified cases-such as healthcare workers or asymptomatic patients-as important vectors of transmission. Evidence is needed to ascertain whether routine screening increases case ascertainment and limits nosocomial transmission.

Keywords: Healthcare-associated infection; Molecular epidemiology; Nosocomial transmission; SARS-CoV-2; Whole-genome sequencing.

Fig. 1

Epidemiological description of cases diagnosed during the first wave of the pandemic. On the left-hand y-axis, the grey bar chart displays new cases over time between 10th March and 31st April 2020. Over the same period the right-hand y-axis shows incidence of nosocomial cases (maroon line). Overlaid are five key dates in public policy and infection control: (A) 13th March: testing recommended for all inpatients with cough and fever; use of aprons, gloves and surgical face masks for interactions with confirmed/suspected cases; (B) 16th March: strong government advice for social distancing; (C) 23rd March: implementation of national lockdown; (D) 25th March: exclusion of hospital visitors, and (E) 28th March: mandatory use of surgical masks for all patient interactions under 2 metres.

Fig. 2

(a) Haplotype representations of the 14 clusters that emerge after applying the clustering process using epidemiological and viral genetic data (see Methods). Clusters are named after the hospital site in which they occurred (leftmost column). Cluster haplotype lineages are shown in black (second column from left). Cluster haplotypes are depicted with a ‘1’ in a given position indicating the presence of the SNP relative to the reference genome shown above in vertical text, and a ‘.’ indicating its absence (wild-type sequence). Cluster rows are coloured based loosely on the similarity of the cluster haplotypes to one another. This same colour scheme is used to represent specific clusters in subsequent figures. (b) Epidemiological clusters 4–33, including cases where n > 2 (Supplementary Material Table S6), are coloured according to how many of their patients belong to a combined epidemiological plus genomics cluster, with the colour indicative of the viral haplotype (Fig. 2a). Patients with viral haplotypes not found in any combined cluster are coloured grey, and those patients for which sequence was unavailable are shown in black. Epidemiological cluster number is shown on the x-axis. Epidemiological clusters 1–3 are not displayed due to their large size. (c) Combined epidemiological plus genomic clusters from the acute and elective hospital sites. Clusters are coloured according to viral genomic haplotype (Fig. 2a). Clusters are shown broken down into ECDC patient nosocomial categories, with different shapes indicating the different categories. Enrichment of the cluster viral haplotype frequency in our study dataset versus the frequency in the community (Supplementary Material Table S7 and Methods) is shown on top of each cluster column.

Fig. 3

Phylogenetic tree (left panel) for sequences with Pangolin lineage assignment B.1. Tree tips are labelled with patient ID, colour-coded according to transmission cluster assigned in our combined epidemiological and genomic investigation. Symbols at the tree tips are displayed according to community-acquired or nosocomial infection classifications. Sequence sample dates are plotted in line with the tree tips using the same symbols in the right-hand panel; admission periods prior to the sample date for each patient are also displayed in this plot as horizontal lines.

Fig. 4

Pictorial representation of ward stays and movements for patients within cluster GUY1. Each row represents a different case. Patient ID, designation, lineage and single-nucleotide polymorphism (SNP) variants are marked. Ward movements between 1st and 31st March are displayed. Different wards are distinguished by given colours. Where there is more than one ward stay on 1 day, the longest ward stay is represented. The sample collection date is marked with an ‘x’. Symptom onset, where known, is marked with a cross ‘†’. Time periods outside of the acquisition period are shaded.