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Characteristics of  SARS-CoV-2 super-spreaders in Japan

Published:December 30, 2021DOI:https://doi.org/10.1016/j.jinf.2021.12.036
      Dear Editor,
      In this Journal, Majra and colleagues reviewed the role of `super-spreaders' in the COVID-19 pandemic
      • Majra D.
      • Benson J.
      • Pitts J.
      • Stebbing J.
      SARS-CoV-2 (COVID-19) superspreader events.
      . Since the time of the SARS-CoV epidemic in 2003 and the MERS-CoV epidemic in 2012, it was found that not all patients were equally infectious; super-spreaders were highly contagious to their surroundings and could transmit the infection
      • Wang Sh X.
      • Li Y.M.
      • Sun B.C.
      • Zhang S.W.
      • Zhao W.H.
      • Wei M.T.
      • et al.
      The SARS outbreak in a general hospital in Tianjin, China – the case of super-spreader.
      . Based on the experience of these two epidemics, a super-spreader can be defined as a patient who has a high viral load, sheds virus for a long period of time, and is not necessarily critically ill
      • Lin J.
      • Yan K.
      • Zhang J.
      • Cai T.
      • Zheng J.
      A super-spreader of COVID-19 in Ningbo city in China.
      . In the Covid-19 pandemic, it has also been noted that high viral load leads to high infectivity
      • He X.
      • Lau E.H.Y.
      • Wu P.
      • Deng X.
      • Wang J.
      • Hao X.
      • et al.
      Temporal dynamics in viral shedding and transmissibility of COVID-19.
      ,
      • Kawasuji H.
      • Takegoshi Y.
      • Kaneda M.
      • Ueno A.
      • Miyajima Y.
      • Kawago K.
      • et al.
      Transmissibility of COVID-19 depends on the viral load around onset in adult and symptomatic patients.
      , and that the presence of super-spreaders is involved in super-spreading events, where some individuals spread to a disproportionate number of individuals, compared to most individuals who infected only a few or none
      • Lin J.
      • Yan K.
      • Zhang J.
      • Cai T.
      • Zheng J.
      A super-spreader of COVID-19 in Ningbo city in China.
      ,
      • Zhang Y.
      • Li Y.
      • Wang L.
      • Li M.
      • Zhou X.
      Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China.
      . It has also been reported that patients with severe disease have higher viral load and longer viral shedding period compared to patients with mild disease
      • Zheng S.
      • Fan J.
      • Yu F.
      • Feng B.
      • Lou B.
      • Zou Q.
      • et al.
      Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study.
      . However, few studies have examined the characteristics of people with high copy number, taking into account the fact that viral load changes over time. To that end, we conducted a single-center, retrospective study in patients diagnosed with COVID-19 and had their PCR copy number measured multiple times (Supplementary materials 1). Between March 2020 and June 2021, patients with a confirmed diagnosis of COVID-19, admitted to Tokyo Medical and Dental University, with polymerase chain reaction (PCR) copy number measured one or more times (median tested times: 2, range 1–26), were included in the study. A total of 379 patients were eligible for the study.
      Table 1 shows the demographic data of the patients. The median age of the patients was 59 years, and about 33% were female. Median number of PCR tests was 2 (range: 1–26). In more than 90% of the patients who had more than one PCR test performed, the viral load was its maximum for that individual at the first or second test. About 59% had underlying disease and about 21% had more than three underlying diseases. Underlying diseases included hypertension (146, 38.5%), diabetes mellitus (82, 21.6%), dyslipidemia (70, 18.5%), hyperuricemia (29, 7.7%), rheumatoid arthritis (8, 2.1%), cancer (71, 18.7%), chronic kidney disease (25, 6.6%), stroke (19, 5.0%), heart disease (including myocardial infarction, atrial fibrillation, chronic heart disease, 34, 9.0%), and lung disease (including asthma and chronic obstructive lung disease, 41, 10.8%). All but one patient in this study were unvaccinated.
      Table 1Demographics of study population (N = 379).
      CharacteristicsN (%) or Median (range)
      AgeMedian (range)59 (20–95)
      ≦2923 (6.1%)
      30–3933 (8.7%)
      40–4953 (14.0%)
      50–5981 (21.4%)
      60–6964 (16.9%)
      70+125 (33.0%)
      SexFemale124 (32.7%)
      Male255 (67.3%)
      Number of PCR testsMedian (range)2 (1–26)
      Number of PCR test performed1177 (46.7%)
      283 (21.9%)
      361 (16.1%))
      4+58 (15.3%)
      Order of test showing the maximum number of copies*11st test129 (63.9%)
      2nd test55 (27.2%)
      3rd+ test18 (8.9%)
      No. days from onset of illness to hospitalizationMedian (range)5 (0–34)
      No. of days in hospitalMedian (range)6 (0–35)
      Number of inpatients per waveFirst wave17 (4.5%)
      Second wave132 (34.8%)
      Third wave125 (33.0%)
      Fourth wave105 (27.7%)
      OutcomeDeath29 (7.7%)
      Transfer105 (27.7%)
      Discharge from hospital245 (64.6%)
      ICU admission139 (36.7%)
      ICU admission periodMedian (range)81–84
      Use of devicesVentilator99 (26.1%)
      Extracorporeal membrane oxygenation (ECMO)11 (2.9%)
      High Flow Nasal Therapy27 (7.1%)
      SARS-CoV-2 variantL452R2 (0.5%)
      N501Y72 (19.0%)
      Pharmaceutical treatmentFavipiravir68 (17.9%))
      Ciclesonide19 (5.0%)
      Nafamostat mesylate3 (0.8%)
      Tocilizumab19 (5.0%)
      Remdesivir163 (43.0%)
      Dexamethasone189 (49.9%)
      Baricitinib16 (4.2%)
      Others26 (6.9%)
      Underlying diseases and conditionsAny224 (59.10%)
      0128 (33.8%)
      190 (23.8%)
      282 (21.6%)
      3+79 (20.8%)
      Hypertension146 (38.5%)
      Diabetes mellitus82 (21.6%)
      Dyslipidemia70 (18.5%)
      Hyperuricemia29 (7.7%)
      Rheumatoid arthritis8 (2.1%)
      Cancer71 (18.7%)
      Chronic kidney disease25 (6.6%)
      Stroke19 (5.0%)
      Heart disease⁎234 (9.0%)
      Lung disease*341 (10.8%)
      Allergy68 (17.94%)
      Pregnancy7 (1.85%)
      Smoking statusNever221 (58.3%)
      Current56 (14.8%)
      Past102 (26.9%)
      Vaccination statusNot at all378 (99.7%)
      Once1 (0.26%)
      *1: Patients with only one test were excluded.
      *2: Heart disease includes myocardial infarction, chronic heart failure, or atrial fibrillation.
      *3: Lung disease includes asthma, chronic obstructive pulmonary disease.
      Table 2 shows the results of multivariate regression analysis between patient characteristics and log-transformed copy number. It was found that patients with diabetes mellitus, rheumatoid arthritis and history of stroke were significantly more likely to have a higher copy number of log of SARS-CoV-2 (coefficient: 1.25 (95% confidence interval (CI): 0.16–2.35), 3.22 (95% CI: 0.14–6.31), and 2.37 (95% CI: 0.34–4.41), respectively). Patients who had three or more underlying diseases were also significantly associated with increased copy number (coefficient: 1.83: (95% CI: 0.45–3.20)) than those without underlying diseases. After adjustment for gender, age, and smoking status, the associations with having three or more diseases, diabetes mellitus, rheumatoid arthritis, and stroke remained statistically significant (model 2). Detailed discussion is presented in Supplementary materials 2.
      Table 2Crude and adjusted regression analysis of the association between patient characteristics and log-transformed copy number.
      CrudeModel 1
      Adjusted for gender, age, and smoking status.
      coefficient95% CIcoefficient95% CI
      Age≦29ref.ref.
      30–390.98−1.34 to 3.311.01−1.26 to 3.29
      40–49−0.24−2.38 to 1.89−0.77−2.89 to 1.36
      50–590.12−1.91 to 2.14−0.52−2.54 to 1.49
      60–690.59−1.49 to 2.67−0.22−2.31 to 1.87
      70+0.97−0.98 to 2.91−0.002−1.96 to 1.96
      SexFemaleref.ref.
      Male0.65−0.28 to 1.590.63−0.38 to 1.63
      Presence of
      underlying disease and conditionsAny (ref: none)0.88−0.01 to 1.77
      Number of
      underlying
      disease and
      conditions0ref.ref.
      1−0.53−1.68 to 0.63−0.50−1.75 to 0.75
      20.12−1.07 to 1.310.07−1.27 to 1.40
      3+1.940.74 to 3.141.830.45 to 3.20
      Specific underlying disease and conditionsHypertension0.60−0.30 to 1.50
      Diabetes mellitus1.550.49 to 2.601.250.16 to 2.35
      Dyslipidemia0.76−0.37 to 1.89
      Hyperuricemia0.49−1.17 to 2.14
      Rheumatoid arthritis3.340.30 to 6.383.220.14 to 6.31
      Cancer0.20−0.93 to 1.32
      Chronic kidney disease1.980.22 to 3.741.32−0.48 to 3.11
      Stroke3.111.12 to 5.102.370.34 to 4.41
      Heart disease0.52−1.02 to 2.06
      Lung disease0.34−1.08 to 1.75
      Allergy−0.40−1.55 to 0.74
      Pregnancy0.95−2.32 to 4.21
      Smoking Status
      Neverref.ref.
      Current0.44−0.84 to 1.720.32−0.97 to 1.61
      Past0.49−0.54 to 1.510.38−0.69 to 1.45
      Bold indicates p < 0.05.
      Regression coefficient and 95% confidence interval (CI) are shown.
      a Adjusted for gender, age, and smoking status.
      This study has several limitations. First, some of the patients included in the analysis were transferred from other hospitals, and it is possible that the copy number of the virus was modified by the treatment before the transfer. Second, the patients we analyzed were moderately to severely ill, and the association of viral load with underlying disease in patients with mild disease is not clear. Third, since the study period was between April 2020 and July 2021, few patients with the Delta variant carrying the L452R mutation were included. Hence, we do not know whether the difference in strains modified the association between the risk factors that were studied and the viral copy number.
      There are several study strengths. First, we have conducted multiple PCR tests over time and have examined the association between possible risk factors and viral copy number using the maximum value, considering that viral load changes over time. Second, in our study, specimens for PCR testing were collected from the nasopharynx by a limited number of medical doctors, so the quality of the specimens was maintained. Third, the duration of the study was longer than one year and the number of patients included was larger than that of previous studies
      • To K.K.
      • Tsang O.T.
      • Leung W.S.
      • Tam A.R.
      • Wu T.C.
      • Lung D.C.
      • et al.
      Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study.
      ,
      • Zheng S.
      • Fan J.
      • Yu F.
      • Feng B.
      • Lou B.
      • Zou Q.
      • et al.
      Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study.
      ,
      • Shi F.
      • Wu T.
      • Zhu X.
      • Ge Y.
      • Zeng X.
      • Chi Y.
      • et al.
      Association of viral load with serum biomakers among COVID-19 cases.
      .
      Clinical implications can be drawn as follows. First, in SARS-CoV-2 infections, it has been reported that infections could occur from super-spreaders who were highly contagious to their surroundings. Thus, our finding will provide useful information for identifying super-spreaders using information on underlying diseases and laboratory data at the time of admission. For example, by identifying potential super-spreaders based on the information, physician could isolate patients in private rooms and elicit the attention of medical personnel to prevent nosocomial cluster outbreaks
      • Li Y.K.
      • Peng S.
      • Li L.Q.
      • Wang Q.
      • Ping W.
      • Zhang N.
      • et al.
      Clinical and transmission characteristics of COVID-19 - A retrospective study of 25 cases from a single thoracic surgery department.
      ,
      • Majra D.
      • Benson J.
      • Pitts J.
      • Stebbing J.
      SARS-CoV-2 (COVID-19) superspreader events.
      . Second, among patients who underwent multiple PCR tests, more than 90% had reached maximum viral load on the first or second test. This suggests that special infection control measures, such as isolation and alerting healthcare workers, need to be taken especially during the early stages, for patients who are thought to be at high risk of higher viral copy numbers based on the information on underlying diseases and laboratory data at the time of admission.
      In moderately to severely ill COVID-19 hospitalized patients, diabetes mellitus, rheumatoid arthritis stroke, and a history of having three or more disease were associated with higher viral load, even after adjusting for age and gender. Lower platelet count and lower CRP levels on admission were also associated with higher viral load (Supplementary Table 1). Our study showed possible characteristics of super-spreaders. More research is needed to address the prevention of secondary infections caused by super-spreaders.

      Authors’ contributions

      Tomoki Kawahara: Software, Visualization, Writing-Original draft preparation.
      Yutaka Ueki: Resources.
      Nobutoshi Nawa: Writing-Review & Editing.
      Shigeru Miyamae: Resources.
      Mariko Hanafusa: Resources, Data curation.
      Yuki Goto: Resources, Data curation.
      Shuji Tohda: Resources.
      Takeo Fujiwara: Conceptualization, Writing-Review & Editing, Supervision.
      All the authors provided intellectual input and approved the final draft of the manuscript.

      Conflict of Interest

      The authors declare no conflict of interest related to this study.

      Funding

      This study has not received any funding or grant.

      Appendix. Supplementary materials

      References

        • Majra D.
        • Benson J.
        • Pitts J.
        • Stebbing J.
        SARS-CoV-2 (COVID-19) superspreader events.
        J Infect. 2021; 82: 36-40
        • Wang Sh X.
        • Li Y.M.
        • Sun B.C.
        • Zhang S.W.
        • Zhao W.H.
        • Wei M.T.
        • et al.
        The SARS outbreak in a general hospital in Tianjin, China – the case of super-spreader.
        Epidemiol Infect. 2006; 134: 786-791
        • Lin J.
        • Yan K.
        • Zhang J.
        • Cai T.
        • Zheng J.
        A super-spreader of COVID-19 in Ningbo city in China.
        J Infect Public Health. 2020; 13: 935-937
        • He X.
        • Lau E.H.Y.
        • Wu P.
        • Deng X.
        • Wang J.
        • Hao X.
        • et al.
        Temporal dynamics in viral shedding and transmissibility of COVID-19.
        Nat Med. 2020; 26: 672-675
        • Kawasuji H.
        • Takegoshi Y.
        • Kaneda M.
        • Ueno A.
        • Miyajima Y.
        • Kawago K.
        • et al.
        Transmissibility of COVID-19 depends on the viral load around onset in adult and symptomatic patients.
        PLoS ONE. 2020; 15e0243597
        • Zhang Y.
        • Li Y.
        • Wang L.
        • Li M.
        • Zhou X.
        Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China.
        Int J Environ Res Public Health. 2020; 17
        • Zheng S.
        • Fan J.
        • Yu F.
        • Feng B.
        • Lou B.
        • Zou Q.
        • et al.
        Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study.
        BMJ. 2020; 369: m1443
        • To K.K.
        • Tsang O.T.
        • Leung W.S.
        • Tam A.R.
        • Wu T.C.
        • Lung D.C.
        • et al.
        Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study.
        Lancet Infect Dis. 2020; 20: 565-574
        • Shi F.
        • Wu T.
        • Zhu X.
        • Ge Y.
        • Zeng X.
        • Chi Y.
        • et al.
        Association of viral load with serum biomakers among COVID-19 cases.
        Virology. 2020; 546: 122-126
        • Li Y.K.
        • Peng S.
        • Li L.Q.
        • Wang Q.
        • Ping W.
        • Zhang N.
        • et al.
        Clinical and transmission characteristics of COVID-19 - A retrospective study of 25 cases from a single thoracic surgery department.
        Curr Med Sci. 2020; 40: 295-300