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Unexpected details regarding nosocomial transmission revealed by whole-genome sequencing of severe acute respiratory coronavirus virus 2 (SARS-CoV-2)

Published online by Cambridge University Press:  20 August 2021

Sofia Myhrman*
Affiliation:
Department of Clinical Microbiology, Infection Control Unit, Region Vastra Gotaland, Sahlgrenska University Hospital, Gothenburg, Sweden
Josefin Olausson
Affiliation:
Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden
Johan Ringlander
Affiliation:
Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden
Linéa Gustavsson
Affiliation:
Department of Geriatric Medicine, Region Vastra Gotaland, Sahlgrenska University Hospital, Gothenburg, Sweden
Hedvig E. Jakobsson
Affiliation:
Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden
Martina Sansone
Affiliation:
Department of Clinical Microbiology, Infection Control Unit, Region Vastra Gotaland, Sahlgrenska University Hospital, Gothenburg, Sweden Department of Infectious Diseases, Region Vastra Gotaland, Sahlgrenska University Hospital, Gothenburg, Sweden
Johan Westin
Affiliation:
Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden Department of Infectious Diseases, Region Vastra Gotaland, Sahlgrenska University Hospital, Gothenburg, Sweden
*
Author for correspondence: Sofia Myhrman, E-mail: sofia.myhrman@vgregion.se
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Abstract

Objective:

Effective infection prevention and control (IPC) measures are key for protecting patients from nosocomial infections and require knowledge of transmission mechanisms in different settings. We performed a detailed outbreak analysis of the transmission and outcome of coronavirus disease 2019 (COVID-19) in a geriatric ward by combining whole-genome sequencing (WGS) with epidemiological data.

Design:

Retrospective cohort study.

Setting:

Tertiary-care hospital.

Participants:

Patients and healthcare workers (HCWs) from the ward with a nasopharyngeal sample (NPS) positive for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) RNA during the outbreak period.

Methods:

Patient data regarding clinical characteristics, exposure and outcome were collected retrospectively from medical records. Stored NPSs from 32 patients and 15 HCWs were selected for WGS and phylogenetic analysis.

Results:

The median patient age was 84 years and 17 (53%) of 32 were male. Also, 14 patients (44%) died within 30 days of sampling. Viral loads were significantly higher among the deceased. WGS was successful in 28 (88%) of 32 patient samples and 14 (93%) of 15 HCW samples. Moreover, 3 separate viral clades were identified: 1 clade and 2 subclades among both patient and HCW samples. Integrated epidemiological and genetic analyses revealed 6 probable transmission events between patients and supported hospital-acquired COVID-19 among 25 of 32 patients.

Conclusions:

WGS provided an insight into the outbreak dynamics and true extent of nosocomial COVID-19. The extensive transmission between patients and HCWs indicated that current IPC measures were insufficient. We recommend increased use of WGS in outbreak investigations to identify otherwise unknown transmission links and to evaluate IPC measures.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Advanced age and multiple comorbidities are risk factors for severe outcomes from coronavirus disease 2019 (COVID-19) Reference Panagiotou, Kosar and White1Reference Wang, Zuo and Liu3 ; hence, outbreaks in geriatric facilities could be devastating. With multiple outbreak reports from healthcare settings Reference Lucey, Macori and Mullane4Reference Chan, Jones and Redmond10 and healthcare-associated COVID-19 infections (HCAI) documented in 5%–59% of hospitalized patients, Reference Carter, Collins and Barlow-Pay2,Reference Wang, Zuo and Liu3,Reference Hall, Clement and Farrow11Reference Jewkes, Zhang and Nicholl13 the need for effective infection prevention and control (IPC) is evident. Long-lasting, close interactions with infected individuals Reference Lee, Wada, Grabowski, Gurley and Lessler14Reference Meyerowitz, Richterman, Gandhi and Sax16 and cluster infections are important drivers of the pandemic. Reference Sun, Wang and Gao15,Reference Liu, Gong and Xiao17 Although social and physical distancing are not practicable within hospitals, measures to prevent and contain cluster infection may be of particular importance. Early case detection is key for outbreak prevention, and presymptomatic or asymptomatic transmission must be considered. Reference Sun, Wang and Gao15,Reference He, Lau and Wu18 Healthcare workers (HCWs) are also important IPC targets because they represent the interface between the healthcare environment and the community and may act as reservoirs, vectors, or victims of transmission. Reference Arons, Hatfield and Reddy5,Reference Asad, Johnston and Blyth7,Reference Sikkema, Pas and Nieuwenhuijse19Reference Ran, Chen, Wang, Wu, Zhang and Tan21

Whole-genome sequencing (WGS) has been used in epidemiological investigations of nosocomial transmission of influenza Reference Sansone, Andersson, Gustavsson, Andersson, Norden and Westin22 and, recently, for severe acute respiratory coronavirus virus 2 (SARS-CoV-2). Reference Lucey, Macori and Mullane4,Reference Klompas, Baker and Rhee9,Reference Chan, Jones and Redmond10,Reference Meredith, Hamilton and Warne12,Reference Paltansing, Sikkema, de Man, Koopmans, Oude Munnink and de Man20,Reference Seemann, Lane and Sherry23 This approach may be challenging for SARS-CoV-2 due to a lower mutation rate than other RNA viruses, Reference Abdelrahman, Li and Wang24 especially if conducted during widespread community transmission. Here, we investigated the usefulness of including WGS and phylogeny for a detailed outbreak analysis of COVID-19 in a geriatric hospital setting.

Methods

Setting

The outbreak occurred during the first wave of the pandemic in a 2,000-bed tertiary-care hospital in western Sweden that serve a population of ˜700,000. The affected 30-bed unit was assigned for orthogeriatric patients without COVID-19 infection and comprised 6 single-bed rooms, 6 two-bed rooms, and 3 four-bed rooms. During the outbreak period, 166 patients received inpatient care at the ward.

Definitions and data collection

The outbreak period was set from the sampling day of the index case until 14 days had passed without any newly discovered cases, considering an incubation period of 2–14 days. A patient or HCW from the ward with a nasopharyngeal sample (NPS) positive for SARS-CoV-2 RNA was considered an outbreak case. Patient data were retrospectively collected from medical records, and information regarding contact tracing and ward occupancy from the IPC team and the hospital administrative unit. No individual data were available for HCWs.

HCAIs were classified according to Meredith et al Reference Meredith, Hamilton and Warne12 as true when confirmed >14 days after admission, days 7–14 after admission (suspected), days 3–6 after admission (indeterminate), and ≤2 days after admission (community associated).

Infection prevention and control measures

In accordance with the Swedish National Health Authority, personal protective equipment (PPE) was recommended only when within 1 m from a suspected or confirmed case of COVID-19 and included a plastic apron and a full-face visor (stretching below the chin) or a surgical mask (IIR) and face shield or googles. A respirator (FFP2-3) was added if aerosol-generating procedures were performed in the room. Gloves and a long-sleeve apron were recommended for those at risk of contact with bodily fluids.

Patients were triaged for COVID-19–associated symptoms upon arrival at the emergency department and were considered suspected cases if they presented at least 2 of the following symptoms: cough, sore throat, fever, and shortness of breath (or upon judgment by the treating physician). Suspected cases were isolated at a quarantine ward until the diagnosis was confirmed or averted. Confirmed cases were transferred to assigned COVID-19 wards. Visitor restrictions were enforced throughout the hospital and admitted patients were restricted to their rooms.

Contact tracing was performed around all confirmed cases. Patients sharing a room with a case during their infectious phase were considered close contacts and were isolated and monitored for symptoms for 14 days. HCWs were considered close contacts when exposed to an infectious case without PPE and continued to work if asymptomatic during the incubation period. HCWs with symptoms of COVID-19 self-quarantined at home for at least 7 days unless they tested negative. Testing resources were limited and prioritized for suspected patient cases requiring in-hospital care.

Laboratory methods and details on bioinformatics and phylogenetic analysis are provided in the Supplementary Material (online).

Ethical statement

Approval for this study was granted by the Swedish Ethical Review Authority (protocol no. 2020-03276).

Results

Outbreak description

In total, 32 patients and 15 HCWs were included in this study (Fig. 1). The index case (patient 1) developed COVID-19 symptoms and was sampled 8 days after admission (outbreak day 0). The first secondary case (patient 2) tested positive on outbreak day 5, 11 days after admission. Close contact between them was excluded. Several staff members reported illness during this period, and HCW–patient transmission was suspected. An outbreak investigation initiated by staff management and the IPC team identified several possible factors contributing to transmission: difficulties in symptom interpretation, crowding in workspaces, PPE shortage and insufficient IPC training. Testing of HCWs was available from outbreak day 14. IPC training and PPE resource allocation was initiated in week 3. By week 5, HCWs used full-face visors in all patient care activities and social gatherings were limited during breaks. The ward closed for new admissions on day 30, and screening of the remaining patients (n = 17) identified 8 cases, of whom 5 were asymptomatic. Repeated screening (n = 9) on day 32 identified 1 additional case.

Fig. 1. Epidemic curve of COVID-19 cases in a hospital ward outbreak. Day of positive SARS-CoV-2 nasopharyngeal samples from 32 patients (above x-axis) and 15 healthcare workers (HCW; below x-axis) are displayed according to timeline throughout the outbreak period. Individual case numbers are shown for patients and HCWs separately. Colors indicate viral clades and arrows the implementation of outbreak control measures.

Case characteristics

Patient characteristics are shown in Supplementary Table 1 (online). The median age was 84 years and 17 (53%) of 32 patients were male. The overall 30-day mortality was 44% (death occurring in median 8 days after sampling). No additional mortality was observed within 90 days, and no cases were lost to follow-up. Viral load was significantly higher among the deceased. Also, 5 asymptomatic patients were identified, of whom 4 developed symptoms within the following 4 days (Supplementary Fig. 1 online). One patient remained asymptomatic within 5 days of follow-up and had a possibly false-positive test due to a very low viral load (Ct value, 39).

Outbreak analysis

WGS was successful in 28 (88%) of 32 patient samples and 14 (93%) of 15 HCW samples. Patient 31 was excluded from phylogenetic analysis due to low viral load. Patients 15 and 23 and HCW 7 were excluded due to lack of material and patient 27 was excluded due to low genomic coverage. Viral strains from 3 genetically distinct clades (20A–C) were found among both outbreak and community sequences, although outbreak sequences showed greater internal genetic similarity (Supplementary Fig. 2a online). Two outliers among patient sequences were found in clade 20B and clade 20C. Based on the phylogenetic clustering, clade 20A was separated into subclades I and II, which also appeared during different phases of the outbreak (Fig. 1). Clade 20AI-II and 20B were dispersed among both patient and HCW sequences (Supplemental Fig. 2b online). These phylogenetic analyses suggest 4 separate introductions, of which 3 (20AI-II and 20B) resulted in secondary transmission.

Contact tracing revealed an epidemiological link (close contact) between 22 of 32 patient cases (Fig. 2). The phylogenetic analysis did not support transmission in 5 of these cases due to clade differences (patients 4, 18, and 21) or sequence differences (patients 9 and 13) (Supplementary Fig. 2b online and Fig. 2). In contrast, for 6 of the 17 remaining cases (patients 11, 17, 20, 25, 27, and 28), a patient–patient transmission event between close contacts was supported by a positive nasopharyngeal sample (NPS) or symptom onset occurring within 2–14 days of each other (Fig. 2).

Fig. 2. Patient–patient transmission of SARS-CoV-2 in a hospital ward outbreak. The panel display 22 patient cases defined as close contacts due to sharing a room with another case. Individual case numbers and letters indicating shared room are seen on the y-axis, and timeline of the outbreak period (days) seen on the x-axis. Bars show day of admission until discharge from the affected ward. Colors represent duration of shared room with another case and viral clade. Dots indicate time point for sampling and stars represent symptom onset. A patient–patient transmission event was probable if: sequence differences did not exclude a genetic relationship, and day of sampling or symptom onset for 2 close contacts occurred within 2–14 days of each other.

Based on the case definitions, Reference Meredith, Hamilton and Warne12 4 of 32 patients had true HCAIs. The phylogenetic analysis revealed a close relationship to other outbreak sequences for 27 of 28 patients (Supplemental Fig. 2a online). The single finding of clade 20C (patient 18, NPS 4 days after admission) supported community transmission. Patient 13 (NPS 6 days after admission, day 20) and HCW 9 (day 30) were genetically similar although more closely related to community sequences than other outbreak sequences. However, the direction of transmission between them is unclear due to lack of clinical data for HCWs. Subclade 20AII was first identified in HCW 3, and patient 1 may have introduced 20AI (Fig. 1). In contrast, clade 20B was unlikely introduced by either patient 4 or 5 because they had no close contact and were sampled the same day (Supplementary Fig. 1 online). Altogether, strong support of true HCAI was found for 25 (78%) of 32 patients.

Discussion

We present an integrated epidemiological and genetic analysis of a COVID-19 hospital outbreak resulting in the discovery of not 1 but multiple separate viral clusters. We also found that instead of 4 patients, 25 patients likely had true HCAI, highlighting both the uncertainties of nosocomial COVID-19 case definitions and the power of WGS.

The mortality in our study was in line with the national mortality rate for COVID-19 in the group aged 80–89 years during this period, 25 although it was higher than previous reports of ˜30% among elderly hospitalized COVID-19 patients. Reference Carter, Collins and Barlow-Pay2,Reference Lucey, Macori and Mullane4,Reference Hagg, Jylhava and Wang26,Reference Rickman, Rampling and Shaw27 COVID-19 has been reported as a significant risk factor of death within 30 days for patients with hip fractures, Reference Hall, Clement and Farrow11,Reference Hall, Clement and Farrow28,Reference Egol, Konda and Bird29 which may have influenced our results. The short median survival time (8 days) corresponds with previous findings, Reference Carter, Collins and Barlow-Pay2,Reference Rickman, Rampling and Shaw27 supporting the finding that death was caused by acute infection. Significantly higher viral load was seen among the deceased patients, previously reported in geriatric patients. Reference De Smet, Mellaerts and Vandewinckele30 The severe outcome stresses the importance to protect this patient group from COVID-19.

Asymptomatic transmission has been suggested a key factor in hospital outbreaks. Reference Asad, Johnston and Blyth7,Reference Lesho, Walsh and Gutowski8 Interpreting symptoms in elderly patients with COVID-19 may be difficult Reference Roxby, Greninger and Hatfield6,Reference Graham, Junghans and Downes31 and asymptomatic cases are more common among patients aged >80 years. Reference Singanayagam, Patel and Charlett32 The asymptomatic cases identified in our study support the insufficiency of a strictly symptom-based testing strategy. We recommend screening combined with serial testing, especially during significant community transmission or when hospital outbreaks are suspected. Reference Klompas, Baker and Rhee9,Reference Taylor, Carter and Lehnertz33

We identified only 6 events of probable patient–patient transmission. This finding suggests that transmission from HCWs might have occurred, which has been reported previously. Reference Lucey, Macori and Mullane4,Reference Asad, Johnston and Blyth7,Reference Klompas, Baker and Rhee9,Reference Meredith, Hamilton and Warne12,Reference Paltansing, Sikkema, de Man, Koopmans, Oude Munnink and de Man20,Reference Taylor, Carter and Lehnertz33 However, the direction is often unclear, and exposure from HCWs seldom appears to result in infection. Reference Baker, Fiumara and Rhee34 The close genetic relationship between sequences from HCWs and patients support the hypothesis that transmission occurred between them; hence, breeches in IPC measures were identified. The symptom-based recommendations overlooked silent transmission from pre- or asymptomatic individuals, and PPE recommendations may have been insufficient. Adherence to IPC measures was unknown. Therefore, the cause of infection could have been inadequate IPC or PPE recommendations, insufficient adherence, or all of these.

The main limitation of this study was the restricted testing policy, which resulted in unrecognized cases among patients and HCWs that might have influenced the course of the outbreak. Establishing the direction of transmission was complicated due to lack of clinical information for HCWs. Patients and HCWs had multiple contacts in other units and transmission outside of the ward may have been overlooked.

The details provided by WGS and phylogeny emphasize the limitations of classic outbreak investigations and the potential of molecular characterization. We recommend increasing the use of WGS for outbreak investigations to clarify transmission links, to identify nosocomial infections, and to evaluate IPC measures.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2021.374

Acknowledgments

We thank the staff at the Department of Clinical Microbiology and Geriatric Medicine, Sahlgrenska University Hospital for their aid in data collection and preparation and all laboratories that originated and submitted sequences to the GISAID database.

Financial support

This work was supported by the Region Vastra Gotaland research fund (grant nos. ALFGBG-67131114 and -719911) and AFA Forsakring (grant no. 200245).

Conflicts of interest

All authors report no conflicts of interest relevant to this article.

Footnotes

a

Authors of equal contribution.

References

Panagiotou, OA, Kosar, CM, White, EM, et al. Risk factors associated with all-cause 30-day mortality in nursing home residents with COVID-19. JAMA Intern Med 2021;181:439448.CrossRefGoogle ScholarPubMed
Carter, B, Collins, JT, Barlow-Pay, F, et al. Nosocomial COVID-19 infection: examining the risk of mortality. The COPE-Nosocomial Study (COVID in Older PEople). J Hosp Infect 2020;106:376384.Google Scholar
Wang, K, Zuo, P, Liu, Y, et al. Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: a cohort study in Wuhan, China. Clin Infect Dis 2020;71:20792088.CrossRefGoogle ScholarPubMed
Lucey, M, Macori, G, Mullane, N, et al. Whole-genome sequencing to track SARS-CoV-2 transmission in nosocomial outbreaks. Clin Infect Dis 2021;72:e727e735.CrossRefGoogle ScholarPubMed
Arons, MM, Hatfield, KM, Reddy, SC, et al. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N Engl J Med 2020;382:20812090.Google Scholar
Roxby, AC, Greninger, AL, Hatfield, KM, et al. Outbreak investigation of COVID-19 among residents and staff of an independent and assisted living community for older adults in Seattle, Washington. JAMA Intern Med 2020;180:11011105.CrossRefGoogle ScholarPubMed
Asad, H, Johnston, C, Blyth, I, et al. Healthcare workers and patients as Trojan horses: a COVID-19 ward outbreak. Infect Prevent Pract 2020;2:100073.CrossRefGoogle Scholar
Lesho, EP, Walsh, EE, Gutowski, J, et al. A cluster-control approach to a coronavirus disease 2019 (COVID-19) outbreak on a stroke ward with infection control considerations for dementia and vascular units. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2020.1437.CrossRefGoogle ScholarPubMed
Klompas, M, Baker, MA, Rhee, C, et al. A SARS-CoV-2 cluster in an acute care hospital. Ann Intern Med 2021;174(6):794802.CrossRefGoogle Scholar
Chan, ER, Jones, LD, Redmond, SN, et al. Use of whole-genome sequencing to investigate a cluster of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in emergency department personnel. Infect Control Hosp Epidemiol 2021. doi: 10.1017/ice.2021.208.CrossRefGoogle ScholarPubMed
Hall, AJ, Clement, ND, Farrow, L, et al. IMPACT-Scot 2 report on COVID-19 in hip fracture patients. Bone Joint J 2021;103-B:888897.CrossRefGoogle ScholarPubMed
Meredith, LW, Hamilton, WL, Warne, B, et al. Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study. Lancet Infect Dis 2020;20:12631271.CrossRefGoogle ScholarPubMed
Jewkes, SV, Zhang, Y, Nicholl, DJ. Nosocomial spread of COVID-19: lessons learned from an audit on a stroke/neurology ward in a UK district general hospital. Clin Med (Lond) 2020;20:e173e177.CrossRefGoogle Scholar
Lee, EC, Wada, NI, Grabowski, MK, Gurley, ES, Lessler, J. The engines of SARS-CoV-2 spread. Science 2020;370:406407.CrossRefGoogle ScholarPubMed
Sun, K, Wang, W, Gao, L, et al. Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. Science 2021;371.Google ScholarPubMed
Meyerowitz, EA, Richterman, A, Gandhi, RT, Sax, PE. Transmission of SARS-CoV-2: a review of viral, host, and environmental factors. Ann Intern Med 2021;174:6979.Google ScholarPubMed
Liu, T, Gong, D, Xiao, J, et al. Cluster infections play important roles in the rapid evolution of COVID-19 transmission: a systematic review. Int J Infect Dis 2020;99:374380.CrossRefGoogle ScholarPubMed
He, X, Lau, EHY, Wu, P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med 2020;26:672675.CrossRefGoogle ScholarPubMed
Sikkema, RS, Pas, SD, Nieuwenhuijse, DF, et al. COVID-19 in healthcare workers in three hospitals in the south of the Netherlands: a cross-sectional study. Lancet Infect Dis 2020;20:12731280.CrossRefGoogle ScholarPubMed
Paltansing, S, Sikkema, RS, de Man, SJ, Koopmans, MPG, Oude Munnink, BB, de Man, P. Transmission of SARS-CoV-2 among healthcare workers and patients in a teaching hospital in the Netherlands confirmed by whole-genome sequencing. J Hosp Infect 2021;110:178183.CrossRefGoogle Scholar
Ran, L, Chen, X, Wang, Y, Wu, W, Zhang, L, Tan, X. Risk factors of healthcare workers with coronavirus disease 2019: a retrospective cohort study in a designated hospital of Wuhan in China. Clin Infect Dis 2020;71:22182221.CrossRefGoogle Scholar
Sansone, M, Andersson, M, Gustavsson, L, Andersson, LM, Norden, R, Westin, J. Extensive hospital in-ward clustering revealed by molecular characterization of influenza A virus infection. Clin Infect Dis 2020;71:e377e383.Google ScholarPubMed
Seemann, T, Lane, CR, Sherry, NL, et al. Tracking the COVID-19 pandemic in Australia using genomics. Nat Commun 2020;11:4376.CrossRefGoogle ScholarPubMed
Abdelrahman, Z, Li, M, Wang, X. Comparative review of SARS-CoV-2, SARS-CoV, MERS-CoV, and influenza A respiratory viruses. Front Immunol 2020;11:552909.CrossRefGoogle ScholarPubMed
COVID-19 statistics. Public Health Agency of Sweden website. https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/. Accessed April 2021.Google Scholar
Hagg, S, Jylhava, J, Wang, Y, et al. Age, frailty, and comorbidity as prognostic factors for short-term outcomes in patients with coronavirus disease 2019 in geriatric care. J Am Med Dir Assoc 2020;21:15551559.CrossRefGoogle ScholarPubMed
Rickman, HM, Rampling, T, Shaw, K, et al. Nosocomial transmission of coronavirus disease 2019: a retrospective study of 66 hospital-acquired cases in a London teaching hospital. Clin Infect Dis 2021;72:690693.CrossRefGoogle Scholar
Hall, AJ, Clement, ND, Farrow, L, et al. IMPACT-Scot report on COVID-19 and hip fractures. Bone Joint J 2020;102-B:12191228.CrossRefGoogle ScholarPubMed
Egol, KA, Konda, SR, Bird, ML, et al. Increased mortality and major complications in hip fracture care during the COVID-19 pandemic: A New York City perspective. J Orthop Trauma 2020;34:395402.CrossRefGoogle ScholarPubMed
De Smet, R, Mellaerts, B, Vandewinckele, H, et al. Frailty and mortality in hospitalized older adults with COVID-19: retrospective observational study. J Am Med Dir Assoc 2020;21:928932.CrossRefGoogle ScholarPubMed
Graham, NSN, Junghans, C, Downes, R, et al. SARS-CoV-2 infection, clinical features and outcome of COVID-19 in United Kingdom nursing homes. J Infect 2020;81:411419.CrossRefGoogle ScholarPubMed
Singanayagam, A, Patel, M, Charlett, A, et al. Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020. Euro Surveill 2020. doi: 10.2807/1560-7917.ES.2020.25.32.2001483.CrossRefGoogle ScholarPubMed
Taylor, J, Carter, RJ, Lehnertz, N, et al. Serial testing for SARS-CoV-2 and virus whole-genome sequencing inform infection risk at two skilled nursing facilities with COVID-19 outbreaks—Minnesota, April–June 2020. Morb Mortal Wkly Rep 2020;69:12881295.CrossRefGoogle ScholarPubMed
Baker, MA, Fiumara, K, Rhee, C, et al. Low risk of COVID-19 among patients exposed to infected healthcare workers. Clin Infect Dis 2020. doi: 10.1093/cid/ciaa1269.Google Scholar
Figure 0

Fig. 1. Epidemic curve of COVID-19 cases in a hospital ward outbreak. Day of positive SARS-CoV-2 nasopharyngeal samples from 32 patients (above x-axis) and 15 healthcare workers (HCW; below x-axis) are displayed according to timeline throughout the outbreak period. Individual case numbers are shown for patients and HCWs separately. Colors indicate viral clades and arrows the implementation of outbreak control measures.

Figure 1

Fig. 2. Patient–patient transmission of SARS-CoV-2 in a hospital ward outbreak. The panel display 22 patient cases defined as close contacts due to sharing a room with another case. Individual case numbers and letters indicating shared room are seen on the y-axis, and timeline of the outbreak period (days) seen on the x-axis. Bars show day of admission until discharge from the affected ward. Colors represent duration of shared room with another case and viral clade. Dots indicate time point for sampling and stars represent symptom onset. A patient–patient transmission event was probable if: sequence differences did not exclude a genetic relationship, and day of sampling or symptom onset for 2 close contacts occurred within 2–14 days of each other.

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