Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study

Abstract

Background:There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings.Methods:We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations.Results:During the study period (1 March to 7 May 2020), we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10% to 19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence (4 patients, 12 HCWs) that jointly led to up to 41 secondary cases; in six additional cases (5 HCWs, 1 patient), importation was possible with a posterior probability between 10% and 50%. Most patient-to-patient transmission events involved patients having shared a ward (95.2%, 95% credible interval [CrI] 84.2%–100%), in contrast to those having shared a room (19.7%, 95% CrI 6.7%–33.3%). Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 2.16, 95% CrI 1.17–4.20, p=0.006); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.72, 95% CrI 0.87–9.00, p=0.06). The proportion of infectors being HCWcovid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCWoutbreak than patientnoso in the late phases (28.6% vs. 11.8%) of the outbreak, although this was not statistically significant.Conclusions:Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This finding highlights gaps in infection control and suggests the possible areas of improvements to limit the extent of nosocomial transmission.Funding:This study was supported by a grant from the Swiss National Science Foundation under the NRP78 funding scheme (Grant no. 4078P0_198363).

Sorting out assortativity: when can we assess the contributions of different population groups to epidemic transmission?

“…Our study underscores the challenges of inferring group contributions in some scenarios, even in the hypothetical instance where transmission trees are perfectly known. Nevertheless, our approach is adaptable and can be extended to reconstructed transmission chains, for example, by estimating the assortativity coefficient across all posterior transmission trees in the setting of Abbas et al [15]. Future research should delve into understanding how uncertainty surrounding these transmission trees further impacts our ability to infer transmission patterns.…”

“…Our approach complements traditional survey-based methods when transmission chains are available. Worby et al .’s relative risk estimation [12], measuring each group’s proportional change in infection incidence before and after the peak, and Abbas et al .’s assessment method [15], comparing actual and expected proportions of infections across groups, do not consider the influence of group size. By integrating group size into our approach, we account for variations in the pool of susceptible individuals within each group, offering a more comprehensive understanding of transmission dynamics.…”

“…Alternatively, transmission assortativity can be directly assessed from observed transmission patterns e.g . by measuring the proportion of cases in different groups [12,13] or by reconstructing the transmission chains [14,15]. These approaches have their own limitations.…”

New frontiers in healthcare environmental hygiene: thoughts from the 2022 healthcare cleaning forum

“…Later, a fatigued workforce and a market flooded with products of varying efficacy would prove to be new challenges for HEH. The disease also spread in unexpected ways; although it was expected that patients would be the cause of nosocomial spread, Covid-19 was often spread through the population of healthcare workers sharing lunch or carpooling to their patients [ 2 ].…”

Reconstruction of SARS-CoV-2 outbreaks in a primary school using epidemiological and genomic data

“…Results from the first part of this study (September to December 2020) indicated transmission occurred mainly between children and among teachers within the school, with occasional spillover to their households [10]. Sequence data of rapidly evolving RNA viruses can provide valuable information on transmission events [11], as also illustrated specifically for SARS-CoV-2 [12]. In this study we make use of the whole genome sequences that were collected from September 2020 to June 2021 to aid in reconstructing the school outbreaks.…”