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Prevalence of asymptomatic COVID-19 infection using a seroepidemiological survey

Published online by Cambridge University Press:  13 November 2020

M. Shakiba
Affiliation:
Cardiovascular Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran School of Health, Guilan University of Medical Sciences, Rasht, Iran
M. Nazemipour
Affiliation:
Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran
A. Heidarzadeh
Affiliation:
Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
M. A. Mansournia*
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
*
Author for correspondence: M. A. Mansournia, E-mail: [email protected], [email protected]
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Abstract

The prevalence of asymptomatic infection by coronavirus disease 2019 (COVID-19) as a critical measure for effectiveness of mitigation strategy has been reported to be widely varied. In this study, we aimed to determine the prevalence of asymptomatic infection using serosurvey on general population. In a cross-sectional seroprevalence survey in Guilan province, Iran, the specific antibody against COVID-19 in a representative sample was detected using rapid test kits. Among 117 seropositive subjects, prevalence of asymptomatic infection was determined based on the history of symptoms during the preceding 3 months. The design-adjusted prevalence of asymptomatic infection was 57.2% (95% confidence interval (CI) 44–69). The prevalence was significantly lower in subjects with previous contacts to COVID-19 patients (12%, 95% CI 2–49) than others without (69%, 95% CI, 46–86). The lowest prevalence was for painful body symptom (74.4%). This study revealed that more than half of the infected COVID-19 patients had no symptoms. The implications of our findings include the importance of adopting public health measures such as social distancing and inefficiency of contact tracing to interrupt epidemic transmission.

Type
Original Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Introduction

Asymptomatic cases who are carriers without any symptoms introduce a major public health challenge in managing COVID-19 pandemic as they can be an important source of infection that undermines control intervention. Asymptomatic infection is considered as a critical parameter for interventions involving contact tracing, because a similar viral load has been reported among asymptomatic and symptomatic cases [Reference Lee1, Reference Zou2] and it has been claimed that there is no difference between the transmissibility of asymptomatic and symptomatic cases among close contacts [Reference Chen3]. Furthermore, a previous study suggested that stricter implemented control measures are needed when the infection duration is long or the probability of a susceptible individual to be asymptomatic is large [Reference Wang4]. The reported estimates of asymptomatic COVID-19 infection are widely varied between 6% and 96% [Reference Oran and Topol5]. So far, the studies attempting to estimate asymptomatic proportion were based on either SARS-CoV-2 testing from close contacts of confirmed COVID-19 patients and high risk groups [Reference Arons6Reference Roxby12] or a sample of infected persons detected by SARS-CoV-2 test in general population [Reference Gudbjartsson13, Reference Lavezzo14]. The former approach may ignore the presence of infection in the general population, and the latter needs to be performed longitudinally to exclude presymptomatic patients. On the contrary, community-based seroepidemiological surveys through detecting specific antibody against SARS-CoV-2 show past infection and can provide better depiction of asymptomatic prevalence and extent of subclinical infection in the population. In this study, we assessed the prevalence of asymptomatic COVID-19 infection (and also separately for each symptom) and associated factors in the whole population using a seroprevalence survey.

Methods

This study was performed on a subset of participants from a seroprevalence survey in the north of Iran. The details of the study were described elsewhere [Reference Shakiba15]. Briefly, a random sample of general population permanently residing in Guilan province, irrespective of age, was selected through stratified multistage (cluster) sampling and during 11–19 April 2020. Residents of institutional living centres such as nursing homes, prisons and boarding schools, and those people who did not want to participate, were under active treatment of COVID-19, or had any contraindication to venepuncture were not invited to the study. On the day of attendance at health care centre, an electronic questionnaire adopted from the WHO [16] including demographic information, comorbidities and series of COVID-19 symptoms that had been present during the preceding 3 months was completed for each participant. Then, a 50 μl of capillary blood was taken from the participants and tested for SARS-CoV-2 specific antibody using VivaDiag COVID-19 IgM/IgG rapid test (VivaChek). The sensitivity of the test was reported to be 83.3% for IgG and both IgM and IgG, and 83.3% for IgM and either IgM or IgG [Reference Cassaniti17]. Of 528 valid blood test results, 117 seropositive subjects were included in this study. Asymptomatic infection was defined as having no symptoms associated with COVID-19 including fever, dry cough, tiredness, chill, sore throat, ache and pain, runny nose, shortness of breath, headache, vomiting, diarrhoea and wheezing. Symptom-specific asymptomatic infection was defined as not developing that symptom. The prevalence of asymptomatic COVID-19 infection and symptom-specific asymptomatic infection with 95% confidence intervals (CIs) were estimated considering the multistage clustering design of the sampling. Specifically, an inverse probability weighting with weights equal to the inverse of the probability of selection was used to adjust for selection bias because the probability of selection varied over the participants [Reference Mansournia and Altman18]. Cluster robust standard errors were used to account for clustering [Reference Mansournia19].

Results

A total of 65 out of 117 seropositive subjects reported no symptoms associated with COVID-19 in 3 months preceding the study. The design-adjusted prevalence of asymptomatic infection was 57.2% (95% CI 44–69). Twenty-two subjects had a history of contacts to confirmed COVID-19 patients. The prevalence of asymptomatic infection in subjects with previous contacts to COVID-19 cases (12%, 95% CI 2–49) was significantly lower than others without previous contacts (69%, 95% CI 46–86). The odds of symptomatic infection in contacts of COVID-19 cases was 17.2 (95% CI 4.3–68.1) times of non-COVID-19 contacts. The resulting odds ratio adjusted for sex, age, place of residence, job, education level, obesity and having comorbidities was 45.9 (95% CI 10.3–206). There was no strong evidence for the association between asymptomatic infection with sex (P-value = 0.20), age group (P-value = 0.69), job (P-value = 0.41), place of residence (urban or rural) (P-value = 0.64), educational level (P-value = 0.95), obesity (P-value = 0.31) or having any comorbidities (P-value = 0.25). Of the 117 subjects whose test was positive, 10 (8.5%) reported typical symptoms, and 44 (38%) reported atypical symptoms. The prevalence estimates of asymptomatic infection with 95% CIs based on COVID-19-like symptoms and combination of typical criteria for COVID-19 used by the WHO (i.e. fever, dry cough and tiredness) are provided in Table 1. The lowest proportion was for painful body (74.4%) that was the most prevalent symptom in seropositive subjects.

Table 1. The prevalence of symptom-specific asymptomatic COVID-19 infection based on symptoms

Discussion

In this study based on a seroprevalence survey, the prevalence of asymptomatic infection of SARS-CoV-2 was 57%, indicating that more than half of the infected patients had no symptoms. Asymptomatic prevalence in previous studies using SARS-CoV-2 testing on general population was estimated to be 43–45% [Reference Gudbjartsson13, Reference Lavezzo14]. The prevalence was conservatively estimated to be 30% after accounting for presymptomatic cases [Reference Oran and Topol5]. The main difference between this study and previous reports is that the current findings were based on serosurvey among a representative sample of general population. However, the sample was not large enough to detect associations between asymptomatic prevalence with underlying variables. Our study showed that the asymptomatic rate was highly dependent on previous contact to COVID-19 cases, and was significantly lower in the contacts of COVID-19 patients compared to non-previous contacts (12% vs. 69%). This is in line with the overall report of 15% for prevalence of asymptomatic infection by a previous meta-analysis conducted on studies exploring close contacts of COVID-19 patients [Reference Byambasuren20] in which about 10–90% of primary asymptomatic persons developed symptoms of COVID-19 after 2–14 days of follow-up [Reference Arons6, Reference Sutton8, Reference Lytras21].

Because of evidence revealing transmission of SARS-CoV-2 from asymptomatic cases to others [Reference Lavezzo14, Reference Lee22], the high prevalence of asymptomatic infection gleaned from this study highlights the importance of social distancing and other protective measures such as wearing face masks in the community. The finding also questioned the efficacy and relying on contact tracing as the only approach for containment of COVID-19 and indicated that only testing those with symptoms may not interrupt epidemic transmission. A previous study showed that 10–15% of cases are expected to generate at least one unidentified secondary case which would need detecting by other means [Reference Keeling, Hollingsworth and Read23].

This study had the advantage of including a wide range of age groups from less than 5 years old to more than 80 years. Despite the lack of significant findings, current study revealed two asymptomatic cases out of four seropositive children less than 5 years and 13 out of 19 people older than 60 years. The evidence about the asymptomatic prevalence in children is very scarce [Reference Arons6] and the findings of current study may shed light on the fact that all age groups are at risk of getting SARS-CoV-2 infection and yet to be asymptomatic.

In current study, since the active COVID-19 cases were excluded from the original research, we adjusted the prevalence using the total hospitalised cases in the study period. The difference in the estimated prevalence was very trivial, changing from 57.2% to 57.4%.

This study had some limitations. Recall bias for minor symptoms is likely. However, recall bias was very likely if symptoms were asked for in a period longer than the previous 3 months. Since the study was conducted right after the peak of the epidemic wave, recall bias for underestimating moderate and severe symptoms was less likely. On the contrary, the overlap of influenza season with the peak of the epidemic wave may result in an overreporting of symptoms that are similar between the two diseases. Second, due to limited sample sizes for some combinations of predictors and asymptomatic infection, the confidence limits were unrealistically large suggesting sparse-data bias [Reference Greenland, Mansournia and Altman24Reference Greenland and Mansournia26]. Finally, imperfect rapid test accuracy in terms of test sensitivity may result in false negatives. We did not adjust for diagnostic accuracy of the test, though specificity was perfect and there is no evidence of association between symptoms and becoming false negative so no adjustment is needed.

Conclusion

This study revealed a high prevalence of asymptomatic infection among seropositive SARS-CoV-2 patients. The asymptomatic prevalence was highly dependent to previous contact to COVID-19 patients. Future studies are needed to clearly elucidate the transmissibility of asymptomatic cases. Yet, because of current evidence of asymptomatic transmission of COVID-19, the current study findings reinforces that adopting control measures such as social distancing and wearing face masks in the community is very critical for managing the epidemic.

Acknowledgement

This research was sponsored by Iran's Ministry of health and performed under a research grant from deputy of research at the Guilan University of Medical Sciences. The authors are grateful to all health centres' employees and personnel for cooperating and conducting the survey as well as all participants that took part in this research.

Data availability statement

The data that support the findings of this study are available from the Guilan University of Medical Sciences. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from the authors with the permission from the Guilan University of Medical Sciences.

References

Lee, S et al. (2020) Clinical course and molecular viral shedding among asymptomatic and symptomatic patients with SARS-CoV-2 infection in a community treatment center in the Republic of Korea. JAMA Internal Medicine 180(11), 16.CrossRefGoogle Scholar
Zou, L et al. (2020) SARS-CoV-2 viral load in upper respiratory specimens of infected patients. The New England Journal of Medicine 382, 11771179.CrossRefGoogle ScholarPubMed
Chen, Y et al. (2020) Epidemiological characteristics of infection in COVID-19 close contacts in Ningbo city. Zhonghua liu xing bing xue za zhi 41, 667671.Google ScholarPubMed
Wang, X et al. (2020) The impact of asymptomatic individuals on the strength of public health interventions to prevent the second outbreak of COVID-19. Nonlinear Dynamics, 110.Google ScholarPubMed
Oran, DP and Topol, Ej (2020) Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review. Annals of Internal Medicine 173(5), 362367.CrossRefGoogle Scholar
Arons, MM et al. (2020) Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. New England Journal of Medicine 382, 20812090.CrossRefGoogle Scholar
Moriarty, LF et al. (2020) Public health responses to COVID-19 outbreaks on cruise ships – worldwide, February–March 2020. Morbidity and Mortality Weekly Report 69, 347352.CrossRefGoogle ScholarPubMed
Sutton, D et al. (2020) Universal screening for SARS-CoV-2 in women admitted for delivery. The New England Journal of Medicine 382, 21632164.CrossRefGoogle ScholarPubMed
Bi, Q et al. (2020) Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases 20, 911919.CrossRefGoogle ScholarPubMed
Cheng, H-Y et al. (2020) Contact tracing assessment of COVID-19 transmission dynamics in Taiwan and risk at different exposure periods before and after symptom onset. JAMA Internal Medicine 180, 11561163.CrossRefGoogle ScholarPubMed
Park, SY et al. (2020) Coronavirus disease outbreak in call center, South Korea. Emerging Infectious Diseases 26(8), 16661670.CrossRefGoogle ScholarPubMed
Roxby, AC et al. (2020) Detection of SARS-CoV-2 among residents and staff members of an independent and assisted living community for older adults – Seattle, Washington, 2020. Morbidity and Mortality Weekly Report 69, 416418.CrossRefGoogle Scholar
Gudbjartsson, DF et al. (2020) Spread of SARS-CoV-2 in the icelandic population. New England Journal of Medicine 382, 23022315.CrossRefGoogle ScholarPubMed
Lavezzo, E et al. (2020) Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'. Nature 584(7821), 425429.CrossRefGoogle ScholarPubMed
Shakiba, M et al. (2021) Seroprevalence of SARS-CoV-2 in Guilan province, Iran, April 2020. Emerging Infectious Disease Journal 27(2). doi: 10.3201/eid2702.201960.CrossRefGoogle Scholar
World Health Organization. Population-based age-stratified seroepidemiological investigation protocol for COVID-19 virus infection 2020 [cited 2020 April 01]. Available at https://www.who.int/publications-detail/population-based-age-stratified-seroepidemiological-investigation-protocol-for-covid-19-virus-infection.Google Scholar
Cassaniti, I et al. (2020) Performance of VivaDiag COVID-19 IgM/IgG rapid test is inadequate for diagnosis of COVID-19 in acute patients referring to emergency room department. Journal of Medical Virology. doi: 10.1002/jmv.25800.CrossRefGoogle ScholarPubMed
Mansournia, MA and Altman, DG (2016) Inverse probability weighting. BMJ 352, i189.CrossRefGoogle ScholarPubMed
Mansournia, MA et al. (in press) Refelections on modern methods: demystifying robust standard errors for epidemiologist. International Journal of Epidemiology.Google Scholar
Byambasuren, O et al. (2020) Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. This paper is on preprint server medRxiv. 2020.05.10.20097543.Google Scholar
Lytras, T et al. (2020) High prevalence of SARS-CoV-2 infection in repatriation flights to Greece from three European countries. Journal of Travel Medicine 27(3), taaa054.CrossRefGoogle ScholarPubMed
Lee, S et al. (2020) Asymptomatic carriage and transmission of SARS-CoV-2: what do we know? Canadian Journal of Anaesthesia 67(10), 14241430.CrossRefGoogle ScholarPubMed
Keeling, MJ, Hollingsworth, TD and Read, JM (2020) Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19). Journal of Epidemiology and Community Health 74, 861866.Google Scholar
Greenland, S, Mansournia, MA and Altman, DG (2016) Sparse data bias: a problem hiding in plain sight. BMJ 352, i1981.CrossRefGoogle ScholarPubMed
Mansournia, MA et al. (2018) Separation in logistic regression: causes, consequences, and control. American Journal of Epidemiology 187, 864870.CrossRefGoogle ScholarPubMed
Greenland, S and Mansournia, MA (2015) Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Statistics in Medicine 34, 31333143.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. The prevalence of symptom-specific asymptomatic COVID-19 infection based on symptoms