1. Medicine
  2. Microbiology and Infectious Disease

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

  1. Mohamed Abbas  Is a corresponding author
  2. Anne Cori
  3. Samuel Cordey
  4. Florian Laubscher
  5. Tomás Robalo Nunes
  6. Ashleigh Myall
  7. Julien Salamun
  8. Philippe Huber
  9. Dina Zekry
  10. Virginie Prendki
  11. Anne Iten
  12. Laure Vieux
  13. Valérie Sauvan
  14. Christophe E Graf
  15. Stephan Harbarth
  1. Infection Control Programme & WHO Collaborating Centre on Patient Safety, Geneva University Hospitals, Switzerland
  2. MRC Centre for Global Infectious Disease Analysis, Imperial College London, United Kingdom
  3. Faculty of Medicine, University of Geneva, Switzerland
  4. Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, United Kingdom
  5. Laboratory of Virology, Department of Diagnostics, Geneva University Hospitals, Switzerland
  6. Serviço de Infecciologia, Hospital Garcia de Orta, EPE, Portugal
  7. Department of Infectious Diseases, Imperial College London, United Kingdom
  8. Department of Mathematics, Imperial College London, United Kingdom
  9. Department of Primary Care, Geneva University Hospitals, Switzerland
  10. Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Switzerland
  11. Division of Infectious Diseases, Geneva University Hospitals, Switzerland
  12. Occupational Health Service, Geneva University Hospitals, Switzerland
https://doi.org/10.7554/eLife.76854
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Cite this article
  1. Mohamed Abbas
  2. Anne Cori
  3. Samuel Cordey
  4. Florian Laubscher
  5. Tomás Robalo Nunes
  6. Ashleigh Myall
  7. Julien Salamun
  8. Philippe Huber
  9. Dina Zekry
  10. Virginie Prendki
  11. Anne Iten
  12. Laure Vieux
  13. Valérie Sauvan
  14. Christophe E Graf
  15. Stephan Harbarth
(2022)
Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study
eLife 11:e76854.
https://doi.org/10.7554/eLife.76854
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10 figures, 5 tables and 1 additional file

Figures

Figure 1 with 1 supplement
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Epidemic curve of the nosocomial COVID-19 outbreak in a geriatric hospital involving HCWs and patients.

Includes eight asymptomatic cases for whom date of onset was inferred (c.f., text). HCW, healthcare worker.

Figure 1—figure supplement 1
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Ward-level epidemic curve.
Figure 2
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Phylogenetic tree of SARS-CoV-2 genome sequences.

The tree includes 148 sequences related to the outbreak (patient and employee sequences are named C1xx [blue] and H10xx [red], respectively), alongside the community cases in the canton of Geneva, Switzerland, that were sequenced in March–April 2020 by the Laboratory of Virology (Geneva University Hospitals) and submitted to GISAID (virus names and accession ID [i.e., EPI_ISL_] are indicated) in the context of an epidemiological surveillance. For each sequence the date of the sample collection is mentioned (yyyy-mm-dd).

Figure 3 with 3 supplements
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Distribution of posterior support of maximum posterior ancestry for all cases, according to identity of (A) individual ancestor, (B) ancestor’s case type (i.e. , ‘HCWcovid’, ‘HCWoutbreak’, ‘patientnoso’, and ‘patientcommunity’), (C) ancestor’s ward, and (D) ancestor’s ward type (i.e., ‘outbreak ward’, ‘non-outbreak ward’).
Figure 3—figure supplement 1
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Ancestry reconstruction (who infected whom) of the outbreaker2 model.

Infectors are on the vertical axis and infectees are on the horizontal axis. Each bubble represents the posterior probability of each infector-infectee transmission pair. The bottom row denotes the probability that an infectee was in fact an imported case. Patients and employees are named C1xx and H10xx, respectively.

Figure 3—figure supplement 1—source code 1

Interactive ancestry plot (who infected whom) – identical to Figure 3—figure supplement 1.

https://cdn.elifesciences.org/articles/76854/elife-76854-fig3-figsupp1-code1-v2.zip
Figure 3—figure supplement 2
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Distribution of number of missed generations across posterior trees, stratified by phase of outbreak.
Figure 3—figure supplement 3
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Comparison of the accuracy of ancestry attribution of each sensitivity analysis.

Each case is on the horizontal axis. The shading corresponds to a value indicating the magnitude of the difference in attribution of the case’s ancestor and the main analysis. For each infectee (case), we calculated the absolute difference in probabilities of infectors (ancestor) between the main analysis and each sensitivity analysis. Values of difference 1 indicate 100% difference in ancestry attribution, and 0 indicate absolute agreement. Sensitivity analysis #1: absence of contact data. Sensitivity analysis #2: longer serial interval (mean 5.2 days, SD 4.7).Sensitivity analysis #3: contacts were based on human resources data for HCWs and on infectious and susceptible periods. Sensitivity analysis #4: patients are considered to be no longer infectious after the date of the positive RT-PCR for SARS-CoV-2. Sensitivity analysis #5: higher value (3) for the threshold for identification of outliers. Sensitivity analysis #6: default value (5) for the threshold for identification of outliers. HCW, healthcare worker.

Figure 4
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Histograms displaying the distributions of secondary cases by each case type (‘HCWcovid’, HCWs working in Covid-19 wards; ‘HCWoutbreak’, HCWs working in outbreak wards; ‘patientnoso’, patients with hospital-acquired Covid-19; ‘patientcommunity’, patients with community-acquired Covid-19) and stratified according to early (up to 9 April 2020) and late phases (as of 10 April 2020).

Number of cases in early phase: HCWoutbreak 19, HCWcovid 43, patientnoso 25, patientcommunity 1. Number of cases in late phase: HCWoutbreak 7, HCWcovid 36, patientnoso 17, patientcommunity 0. HCW, healthcare worker.

Figure 5
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Proportions of transmissions (fcase) attributed to each case type (HCWcovid, HCWoutbreak, patientnoso, and patientcommunity) for each of the 1000 posterior trees retained.

The blue histograms indicate the expected random distributions of fcase, given the prevalence of each case type. The red histograms show the observed distribution of fcase, across 1000 transmission trees reconstructed by outbreaker2. (A) All cases. (B) Transmission to HCWs in Covid-19 wards only. (C) Transmission to HCWs in non-Covid-19 wards (i.e., outbreak wards) only. (D) Transmission to patients with nosocomial Covid-19 only. HCW, healthcare worker.

Appendix 1—figure 1
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Ward movements for patients involved in a cluster.

Each row corresponds to a patient, and the solid lines indicate hospitalisation dates. The lines are coloured according to which ward a patient was in on a particular day. Outbreak wards (A–D) are coloured differently from non-outbreak wards (Q–Z).

Appendix 1—figure 2
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Ward-to-ward transmission matrix.

The matrix indicates the sum of transmission events across all posterior trees from cases in ‘infector’ wards (vertical axis) to cases in ‘infectee’ wards (horizontal axis). The degree of shading is proportional to the estimated posterior number of transmissions for each ward-to-ward pair. Outbreak wards: A–D; non-outbreak wards: P–Z (Z is ‘all wards’).

Appendix 1—figure 3
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Proportions of transmissions attributed to (A) outbreak (foutbreak-ward) and (B) non-outbreak (fnon-outbreak-ward) wards.

The blue histograms indicate the expected random distributions of fward, given the proportion of HCWs amongst cases. The red histograms show the observed distribution of fward, across 1000 transmission trees reconstructed by outbreaker2. (A). All wards. (B) Transmission to outbreak wards only. (C) Transmission to non-outbreak wards only.

Author response image 1
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Tables

Table 1
Characteristics of Covid-19 patients with nosocomial acquisition.
CharacteristicsAll patients(N=49)
Female, n (%)28 (57.1)
Age, median (IQR)85.4 (83.5–89.3)
Asymptomatic, n (%)3 (6.1)
Onset of symptoms before swab date, n (%)12 (24.5)
Days from onset of symptoms to swab, median (IQR)0 (0–0)
Days from onset of symptoms to swab, mean (SD)–0.29 (2.19)
Table 2
Characteristics of SARS-CoV-2 RT-PCR positive healthcare workers.
CharacteristicsAll HCWs(N=127)
Female, n (%)92 (72.4)
Age, median (IQR)32.0 (43.3–54.8)
Profession, n (%)
Nurse57 (44.9)
Nurse assistant39 (30.7)
Doctor19 (15.0)
Care assistant4 (3.2)
Transporter4 (3.2)
Physical therapist2 (1.6)
Speech therapist1 (0.8)
Medical student1 (0.8)
Asymptomatic, n (%) missing data for 55 (3.9)
Days from onset of symptoms to swab, median (IQR)1 (−2 to 21)
 HCWs in Covid-19 wards (HCWcovid)1 (1–2)
 HCWs in non-Covid (outbreak) wards (HCWoutbreak)1 (0–3)
Days from onset of symptoms to swab, mean (SD)1.91 (2.86)
 HCWs in Covid-19 wards (HCWcovid)1.60 (1.78)
 HCWs in non-Covid (outbreak) wards (HCWoutbreak)2.88 (4.84)
Table 3
Imported cases and secondary infections, patients and HCWs are named C1xx and H10xx, respectively.
Imported casePosterior probability of importationSecondary onward transmission by imported casePosterior probability of onward transmission
C107100H1077
C131
C124
H1005
C125
H1034
H1068
C112
C116		100
72.5
39.4
35.0
32.9
27.3
18.5
15.9
11.7
C114*42.5C115*42.5
C115*57.5C114*57.5
C12396.4H1058
H1036
H1047		90.1
16.0
11.1
C153*51.7H1057*51.6
H1008*85.7H1059*85.7
H1011*65.9H1019*61.7
H1012100N/AN/A
H1013100N/AN/A
H1015100N/AN/A
H1017*52.3H1020
H1021*		100.0
52.3
H1019*34.1H1011*28.2
H1021*47.7H1017*47.7
H102586.6H1085
H1031		95.7
41.5
H1048100N/AN/A
H1052*18.9H1082*18.9
H1057*48.3C153*48.3
H1059*14.3H1008*14.3
H1073100N/AN/A
H1082*81.1H1052*81.1
H111085.1H1063
H1064
H1041
H1024
H1091
H1065		58.0
32.4
30.0
22.5
22.1
17.2
H112284.5C113
C104
H1033
H1003
H1004
H1044		53.7
26.0
17.0
15.5
15.0
10.6

  1. N/A: not applicable.

  2. *

    Uncertainty in transmission (i.e., case could either be an imported case or a secondary case).

Appendix 1—table 1
Composition of baseline outbreaker2 model and different sensitivity analyses.
Scenario typeOnset of symptomsGenetic dataContact dataShort serial intervalLonger serial intervalLow outlier thresholdMedium outlier thresholdDefault outlier threshold
Baseline scenarioXXXX X  
Sensitivity analyses
1.XX X X  
2.XXX XX  
3.XXX*X X  
4.XXX†X X  
5.XXXX  X 
6.XXXX   X
  1. *

    For this model, we used the HR data for healthcare worker presence (with some corrections).

  2. †

    For this model, we assumed that patients were no longer infectious after the date of positive RT-PCR.

Appendix 1—table 2
Proportions of secondary infections (i.e., individual R) for each case type (patientnoso, HCWoutbreak, nd HCWcovid) in early (up to 9 April 2020) and late (as of 10 April 2020) phases of the study.

The p-values are for chi-squared tests on these proportions.

PatientnosoHCWoutbreakHCWcovidp-value(HCWoutbreak vs. patientnoso)
Main analysis
≥1 secondary transmission
 Early phase0.68 (0.52–0.8)0.684 (0.526–0.789)0.442 (0.349–0.535)0.576
 Late phase0.471 (0.294–0.647)0.429 (0.286–0.714)0.417 (0.306–0.528)0.552
≥2 secondary transmissions
 Early phase0.32 (0.2–0.44)0.316 (0.158–0.474)0.186 (0.093–0.256)0.459
 Late phase0.118 (0–0.294)0.286 (0–0.571)0.139 (0.056–0.222)0.807
 
Sensitivity analysis #1 (no assumptions about contacts)
≥1 secondary transmission
 Early phase0.64 (0.48–0.76)0.632 (0.474–0.789)0.442 (0.326–0.512)0.527
 Late phase0.529 (0.353–0.706)0.429 (0.143–0.714)0.389 (0.278–0.5)0.322
≥2 secondary transmissions
 Early phase0.36 (0.24–0.52)0.263 (0.105–0.421)0.163 (0.093–0.256)0.190
 Late phase0.176 (0.059–0.353)0.143 (0–0.429)0.111 (0.028–0.195)0.527
 
Sensitivity analysis #2 (long serial interval)
≥1 secondary transmission
 Early phase0.64 (0.52–0.76)0.684 (0.526–0.791)0.442 (0.349–0.535)0.669
 Late phase0.471 (0.294–0.647)0.429 (0.282–0.714)0.389 (0.278–0.5)0.561
≥2 secondary transmissions
 Early phase0.36 (0.2–0.48)0.316 (0.158–0.474)0.186 (0.116–0.279)0.445
 Late phase0.118 (0–0.294)0.286 (0–0.571)0.139 (0.056–0.222)0.790
 
Sensitivity analysis #3 (calibrating contacts based on assumptions on infectiousness)
≥1 secondary transmission
 Early phase0.68 (0.56–0.8)0.684 (0.526–0.789)0.442 (0.349–0.535)0.433
 Late phase0.471 (0.235–0.647)0.286 (0.143–0.571)0.361 (0.25–0.472)0.249
≥2 secondary transmissions
 Early phase0.4 (0.24–0.52)0.368 (0.211–0.474)0.186 (0.116–0.256)0.360
 Late phase0.118 (0–0.294)0.143 (0–0.429)0.083 (0.028–0.167)0.702
 
Sensitivity analysis #4 (patients no longer infectious after date of swab)
≥1 secondary transmission
 Early phase0.64 (0.48–0.8)0.684 (0.526–0.842)0.465 (0.349–0.535)0.627
 Late phase0.471 (0.294–0.706)0.429 (0.286–0.714)0.417 (0.306–0.528)0.522
≥2 secondary transmissions
 Early phase0.32 (0.16–0.44)0.316 (0.158–0.475)0.186 (0.116–0.279)0.541
 Late phase0.118 (0–0.294)0.286 (0–0.571)0.111 (0.028–0.194)0.781
 
Sensitivity analysis #5 (higher value for outlier threshold)
≥1 secondary transmission
 Early phase0.64 (0.48–0.721)0.684 (0.526–0.789)0.419 (0.326–0.512)0.683
 Late phase0.471 (0.294–0.647)0.429 (0.143–0.714)0.417 (0.306–0.528)0.542
≥2 secondary transmissions
 Early phase0.32 (0.16–0.48)0.316 (0.158–0.474)0.186 (0.093–0.256)0.407
 Late phase0.118 (0–0.294)0.286 (0–0.571)0.111 (0.028–0.194)0.774
 
Sensitivity analysis #6 (default value for outlier threshold)
≥1 secondary transmission
 Early phase0.64 (0.48–0.76)0.684 (0.526–0.789)0.442 (0.349–0.512)0.682
 Late phase0.471 (0.294–0.647)0.429 (0.286–0.714)0.417 (0.306–0.528)0.522
≥2 secondary transmissions
 Early phase0.32 (0.2–0.48)0.316 (0.158–0.474)0.186 (0.116–0.279)0.424
 Late phase0.118 (0–0.294)0.286 (0–0.571)0.139 (0.056–0.222)0.790

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  1. Mohamed Abbas
  2. Anne Cori
  3. Samuel Cordey
  4. Florian Laubscher
  5. Tomás Robalo Nunes
  6. Ashleigh Myall
  7. Julien Salamun
  8. Philippe Huber
  9. Dina Zekry
  10. Virginie Prendki
  11. Anne Iten
  12. Laure Vieux
  13. Valérie Sauvan
  14. Christophe E Graf
  15. Stephan Harbarth
(2022)
Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study
eLife 11:e76854.
https://doi.org/10.7554/eLife.76854