Skip to main content
Download PDF

Duration of SARS-CoV-2 RNA positivity from various specimens and clinical characteristics in patients with COVID-19: a systematic review and meta-analysis

Inflammation and Regeneration volume 42, Article number: 16 (2022) Cite this article

Abstract

Background

The duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA positivity will be important to prevent the spread of coronavirus disease 2019 (COVID-19). A systematic review and meta-analysis were conducted following PRISMA to determine the duration from several parts of the body and clinical characteristics affecting it.

Main text

PubMed, Web of Science, Scopus, and CENTRAL were searched for original studies reporting the duration from COVID-19 onset to the disappearance of viral RNA. Of the 1682 studies identified, 100 met the selection criteria and 13,431 patients were included in this study. The duration of SARS-CoV-2 RNA positivity was 18.29 [95% confidence interval: 17.00–19.89] days in the upper respiratory tract samples, 23.79 [20.43–27.16] days in the sputum, 14.60 [12.16–17.05] days in the blood, and 22.38 [18.40–26.35] days in the stool. Sensitivity analysis revealed that the duration was positively correlated with age, comorbidities, severity, and usage of glucocorticoid. Subgroup analysis indicated that the presence or absence of complications had the greatest impact on the difference in DSRP.

Conclusions

The duration of SARS-CoV-2 RNA positivity was 18.29 days in the upper respiratory tract samples. The duration in the sputum and the stool was longer, while that in the blood was shorter. The duration in the upper respiratory tract samples was longer in older, with any comorbidities, severer, and treated with glucocorticoid. These results provide the basic data for the duration of SARS-CoV-2 RNA positivity, and in the future, the effect of vaccination against SARS-CoV-2 and the SARS-CoV-2 variants on the duration of RNA positivity should be assessed.

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 was first reported in China in December 2019 and became a pandemic [1]. Every country took infection control measures (e.g., lockdown), but the number of patients with COVID-19 increased worldwide. The quarantine period for COVID-19 varies from country to country. For example, the Centers for Disease Control and Prevention recommends 5 days for the general population [2]; the Ministry of Health, Labor and Welfare in Japan recommends 10 days from the onset [3]; and the China’s zero-COVID strategy recommends a longer period [4]. The result of reverse transcription-polymerase chain reaction (RT-PCR) is included in the de-quarantine criteria in Japan [3]. Detailed information on the duration of SARS-CoV-2 RNA positivity (DSRP) in various specimens of patients with COVID-19 will be very helpful in infection control.

SARS-CoV-2 RNA is detected in various samples such as nasal mucus, sputum, conjunctiva, blood, urine, gastric fluid, and stool [5]. It is certain that approximately 2 weeks after the onset was required for SARS-CoV-2 RNA to disappear from the respiratory tract in some studies [6, 7], but some cases were reported in which SARS-CoV-2 RNA had continued to be detected for a longer period [8]. The DSRP from other samples remains unclear due to the limited information. Moreover, whether the DSRP in patients with COVID-19 is affected by clinical characteristics remains unknown.

A systematic review of studies reporting the DSRP in patients with COVID-19 has been conducted and the DSRP from various specimens (nasal mucus, sputum, blood, and stool) was determined by a meta-analysis. Moreover, the influence of clinical features such as age, gender, comorbidity, severity, treatment, and locality on the DSRP was also evaluated for identification of the factors affecting the prolongation of DSRP.

Methods

Registration

This meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [9] and registered with PROSPERO (CRD42020193268).

Search strategy

Articles published until December 31, 2020, were searched for on PubMed, Web of Science, Scopus, and Cochrane CENTRAL using the search terms [(COVID-19 OR SARS-CoV-2) AND (shedding OR “viral load” OR clearance) AND patient NOT review] with no language restriction. The searches were performed thrice and the final searches were performed on February 15, 2021.

Selection criteria

The inclusion criteria are studies of human subjects, original articles (not reviews), title or abstract consisting of the terms “COVID-19,” “SARS-CoV-2,” “shedding,” “viral load,” or “clearance,” and linkage to the full text of the article. Studies without raw data to calculate the mean and standard deviation (SD) of the DSRP were excluded. Case reports reporting one or two cases were excluded because it was difficult to calculate the mean and SD. Redundancies between the search sites were eliminated.

Quality assessment

The quality assessment was performed following the study quality assessment tools (Quality Assessment Tool for Case Series Studies) from the National Heart, Lung, and Blood Institute (NHLBI) [10]. The evidence level was evaluated based on the Oxford Centre for Evidence-Based Medicine 2011 [11]. Funnel plots were used to assess publication bias.

Data extraction

Author, year of publication, observational period, the country where the study was conducted, study design, number of patients, age, percentage of females, severity, treatment, comorbidity, and specimen were extracted. The severity was basically quoted from the severity classification used in each paper. In the studies not reporting it, the severity was classified according to the COVID-19 clinical classification released by the National Health Commission of China [12]. The DSRP was defined as the number of days from the appearance of symptoms to the first negative result of RT-PCR, not antigen test, without converting positive thereafter. The Ct (threshold cycle) value to be judged negative was quoted from the criteria used in each paper. The mean and SD of DSRP were extracted. In the studies reporting only the median and interquartile range (IQR) or range of DSRP, the mean and SD were calculated from them using the methods of Wan et al. [13]. Patients whose RT-PCR result for SARS-CoV-2 did not turn negative during the observation period were excluded. Asymptomatic patients were excluded because defining the onset was difficult. The values were manually calculated using information available in the published graphs and tables when raw data were unavailable.

Statistics

In the meta-analysis, the DSRP were expressed as the mean number of days and 95% confidence intervals (CIs). The mean differences were calculated using the random effects model. I2 values of 25%, 50%, and 75% were defined as low, moderate, and high, respectively [14]. The sensitivity analyses were performed based on age, gender, comorbidities, compromised status, severity, and use of glucocorticoid. Spearman’s correlation coefficient was calculated and p values ≤0.05 were considered statistically significant. The subgroup analyses were performed between the patients with different ages, the patients with or without any comorbidities, the patients with different severities, the patients treated with and without glucocorticoid, and the studies from different countries. All analyses were conducted using the R version 4.0.0 (R Project for Statistical Computing) and EZR version 1.42 [15].

Results

Study selection

The current study identified 1682 records from four search sites (927, 666, 918, and 363 studies on PubMed, Web of Science, Scopus, and Cochrane Central Register of Controlled Trials (CENTRAL), respectively). One thousand forty studies which did not meet the inclusion criteria were removed and 542 studies were removed based on the exclusion criteria. Finally, 100 studies met the selection criteria and were included in this meta-analysis (Fig. 1, [5,6,7,8, 16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111]).

Fig. 1
figure 1

PRISMA flow diagram

Full size image

The characteristics of the studies, clinical characteristics, and quality assessment

Most studies were observational studies and were classified as case accumulation research from the viewpoint of the current study. Seventy-two, 15, and 13 studies were reported from China, Asian countries except for China, and European countries, respectively. The start of the observation period was December 29, 2019, to April 30, 2020, and the end was January 11, 2020, to June 10, 2020. The number of patients ranged from 3 to 1320 and the total number of patients with COVID-19 in the 100 studies was 13,431. The median age ranged from 6 to 74.5, with a minimum age of 0 to 49 years and a maximum age of 11 to 96 years. The proportion of women was 0 to 100%. The proportion of patients with any comorbidities was 6.3 to 100%. The proportion of severe patients ranged from 0 to 100%. The proportion of patients treated for COVID-19 with glucocorticoid ranged from 0 to 100%.

The total score of the study quality assessment tools (Quality Assessment Tool for Case Series Studies) from the NHLBI was in the range of 6 to 9 in each study (data not shown). The funnel plots in the upper respiratory tract samples including nasal swab and throat swab (Fig. 2a), sputum (Fig. 2b), blood (Fig. 2c), and stool (Fig. 2d) had asymmetrical isosceles, suggesting the presence of bias or systemic heterogeneity.

Fig. 2
figure 2

The funnel plots of the duration of SARS-CoV-2 RNA positivity in various samples. The funnel plots of the duration of SARS-CoV-2 RNA positivity in the upper respiratory tract samples (a), the sputum (b), the blood (c), and the stool (d) were shown

Full size image

Duration of SARS-CoV-2 RNA positivity on various respiratory tract samples

In all respiratory tract samples including nasal swab, throat swab, sputum, and bronchoalveolar lavage fluid, 11,639 patients from 99 studies were analyzed [5,6,7,8, 16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36, 38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111] with a DSRP of 18.79 days (95% CIs, 17.69–19.89 days, I2 = 99%). In the upper respiratory tract samples including nasal swab and throat swab, 9635 patients from 84 studies were analyzed with a DSRP of 18.29 days (95% CIs, 17.00–19.58 days, I2 = 99%; Fig. 3a). In the nasal swabs, 4042 patients from 32 studies were analyzed with a DSRP of 19.34 days (95% CIs, 16.60–22.07 days, I2 = 99%). In the throat swabs, 4631 patients from 44 studies were analyzed with a DSRP of 17.85 days (95% CIs, 16.43–19.26 days, I2 = 98%). In the sputum, 643 patients from 10 studies were analyzed with a DSRP of 23.79 days (95% CIs, 20.43–27.16 days, I2 = 93%; Fig. 3b). The DSRP on upper respiratory tract samples and sputum of 79 and 57 patients, respectively, were directly compared. The DSRP in the sputum tended to be 3.15 days longer (95% CIs, − 2.26–8.55 days, p < 0. 01, I2 = 81%; Fig. 3c) than the upper respiratory tract samples, but there was no significant difference.

Fig. 3
figure 3

Forest plot: a meta-analysis of the duration of SARS-CoV-2 RNA positivity in the respiratory tract samples. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the upper respiratory tract samples (a) and the sputum (b) was calculated using the random effects model. The difference in the duration of SARS-CoV-2 RNA positivity between the sputum and the upper respiratory samples was calculated using the random effects model (c). Experimental meant the sputum and control meant the upper respiratory tract samples. MRAW, the raw data of mean; 95%-CI, 95% confidence interval; SD, standard deviation; MD, mean difference

Full size image

Duration of SARS-CoV-2 RNA positivity on samples from blood and stool

In the blood, 385 patients from four studies were analyzed with a DSRP of 14.60 days (95% CIs, 12.16–17.05 days, I2 = 88%; Fig. 4a). The DSRP on the blood and the upper respiratory tract samples from 335 and 388 patients, respectively, were directly compared, and there was no significant difference (2.42 days; 95% CIs − 4.11–8.95 days, p < 0.01, I2 = 97%; Fig. 4b). In the stool, 620 patients from 13 studies were analyzed with a DSRP of 22.38 days (95% CIs, 18.40–26.35 days, I2 = 97%; Fig. 4c). The DSRP on the stool and the upper respiratory tract samples from 568 and 644 patients, respectively, were directly compared. The DSRP on the stool was significantly 5.41 days longer (95% CIs, 2.80–8.02 days, p < 0.01, I2 = 86%) than the upper respiratory tract samples (Fig. 4d).

Fig. 4
figure 4

Forest plot: a meta-analysis of the duration of SARS-CoV-2 RNA positivity in various samples. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the blood (a) and stool (c) was calculated using the random effects model. The difference in the duration of SARS-CoV-2 RNA positivity between the blood and upper respiratory tract samples was calculated using the random effects model (b). Experimental meant the blood samples and control meant the upper respiratory tract samples. The difference in the duration of SARS-CoV-2 RNA positivity between the stool and upper respiratory tract samples was calculated using the random effects model (d). Experimental meant the stool and control meant the upper respiratory tract samples. MRAW, the raw data of mean; 95%-CI, 95% confidence interval; SD, standard deviation; MD, mean difference

Full size image

Sensitivity analysis based on the clinical characteristics in upper respiratory tract samples

In the upper respiratory tract samples, sensitivity analyses were performed. The mean age was significantly positively correlated with the DSRP (ρ = 0.22, p = 0.05; Fig. 5a), while the proportion of women was not (ρ = − 0.14, p = 0.19; Fig. 5b). The proportion of patients with any comorbidities was significantly positively correlated with the DSRP (ρ = 0.35, p = 0.02; Fig. 5c), while the proportion of patients with compromised status such as malignancy, human immunodeficiency virus infection, and dialysis treatment was not (ρ = 0.14, p = 0.32; Fig. 5d). The proportion of severe patients was significantly positively correlated with the DSRP (ρ = 0.26, p = 0.02; Fig. 5e), and the proportion of patients treated with glucocorticoid was significantly positively correlated with the DSRP (ρ = 0.26, p = 0.04; Fig. 5f). It was suggested that the age, comorbidities, severity, and usage of glucocorticoid affected the DSRP, and the percentage of patients with any comorbidities had the greatest impact on DSRP based on the value of ρ.

Fig. 5
figure 5

Sensitivity analysis based on the clinical characteristics in upper respiratory tract samples. The association between the duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in upper respiratory tract samples and mean age (a), the proportion of women (b), the proportion of patients with any comorbidities (c), the proportion of patients with compromised status (d), the proportion of severe patients (e), and the proportion of patients treated with glucocorticoid (f) in each study. The correlation was evaluated using the Spearman correlation coefficient

Full size image

Subgroup analysis based on the age, comorbidities, severity, and usage of glucocorticoid in the upper respiratory tract samples

Seven hundred forty-two patients over the age of 60 (older group) from 11 studies were analyzed with a DSRP of 21.24 days (95% CIs, 14.06–28.41 days, I2 = 99%; Fig. 6a). One thousand one hundred twenty-nine patients under the age of 60 (younger group) from 22 studies were analyzed with a DSRP of 16.95 days (95% CIs, 13.56–20.35 days, I2 = 98%; Fig. 6b). The mean age was 68.03 ± 3.12 years in the older group and 36.41 ± 12.05 years in the younger group. The proportion of patients with any comorbidities was 44.79 ± 20.23% in the older group and 28.06 ± 26.85% in the younger group. The proportion of severe patients was 61.90 ± 40.50% in the older group and 22.27 ± 31.21% in the younger group. The proportion of patients treated with glucocorticoid was 37.50 ± 47.87% in the older group and 13.26 ± 26.48% in the younger group. Due to many missing data values, the number of patients in the older group was less than 30 after further adjustment of the patient background. It was judged that the analysis would not be appropriate.

Fig. 6
figure 6

Forest plot: a meta-analysis of the duration of SARS-CoV-2 RNA positivity in the upper respiratory tract sample based on age and comorbidities. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the patients over the age of 60 (a), patients under the age of 60 (b), patients with any comorbidities (c), patients without any comorbidities patients (d) was calculated using the random effects model. MRAW, the raw data of mean; 95%-CI, 95% confidence interval

Full size image

One hundred eighty patients with any comorbidities (comorbidity group) from 13 studies were analyzed with a DSRP of 20.26 days (95% CIs, 17.60–22.92 days, I2 = 93%; Fig. 6c). Two hundred sixty-five patients without any comorbidities (noncomorbidity group) from 10 studies were analyzed with a DSRP of 14.66 days (95% CIs, 12.63–16.69 days, I2 = 85%; Fig. 6d). The mean age was 57.10 ± 8.94 years in the comorbidity group and 37.88 ± 5.76 years in the noncomorbidity group. The proportion of severe patients was 46.67 ± 37.75% in the comorbidity group and 36.55 ± 44.59% in the noncomorbidity group. The proportion of patients treated with glucocorticoid was 8.87 ± 14.41% in the comorbidity group and 27.11 ± 41.71% in the noncomorbidity group. Due to many missing data values, the numbers of patients in both groups were less than 30 after further adjustment of the patient background. It was judged that the analysis would not be appropriate.

One thousand three hundred thirty-nine severe patients from 27 studies were analyzed with a DSRP of 20.79 days (95% CIs, 18.03–23.55 days, I2 = 98%; Fig. 7a). Four thousand two hundred nineteen nonsevere patients from 36 studies were analyzed with a DSRP of 16.36 days (95% CIs, 14.07–18.66 days, I2 = 99%; Fig. 7b). The mean age was 57.16 ± 6.01 in the severe patients and 44.12 ± 11.17 years in the nonsevere patients. The proportion of patients with any comorbidities was 51.05 ± 28.73% in the severe patients and 28.15 ± 12.91% in the nonsevere patients. The proportion of patients treated with glucocorticoid was 21.74 ± 39.91% in the severe patients and 20.43 ± 31.49% in the nonsevere patients. To adjust those factors as further as possible between the severe patients and the nonsevere patients, studies with the mean age of 40 years or older and the proportion of patients with any comorbidities of 30% or more were selected. One hundred seventy-one severe patients were analyzed with a DSRP of 21.53 days (95% CIs 17.57–25.50 days, p < 0.01, I2 = 91%; Fig. 7c). One hundred seventy-five nonsevere patients were analyzed with a DSRP of 20.08 days (95% CIs 15.87–24.29 days, p < 0.01, I2 = 91%; Fig. 7d). It was suggested that the severity of COVID-19 had a mild effect on the DSRP.

Fig. 7
figure 7

Forest plot: a meta-analysis of the duration of SARS-CoV-2 RNA positivity in the upper respiratory tract samples based on the severity. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the severe patients (a) and the nonsevere patients (b) was calculated using the random effects model. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the severe patients (c) and the nonsevere patients (d) from the studies with the mean age of 40 years or older and the proportion of patients with any comorbidities of 30% or more was calculated using the random effects model. MRAW, the raw data of mean; 95%-CI, 95% confidence interval

Full size image

Six hundred forty patients treated with glucocorticoid (glucocorticoid group) from 15 studies were analyzed with a DSRP of 19.72 days (95% CIs, 17.92–21.52 days, I2 = 92%; Fig. 8a). One thousand six hundred seventy patients treated without glucocorticoid (no glucocorticoid group) from 30 studies were analyzed with a DSRP of 15.64 days (95% CIs, 14.18–17.10 days, I2 = 96%; Fig. 8b). The mean age was 52.64 ± 6.28 years in the glucocorticoid group and 46.25 ± 12.68 years in the no glucocorticoid group. The proportion of patients with any comorbidities was 24.89 ± 10.98% in the glucocorticoid group and 45.27 ± 31.88% in the no glucocorticoid group. The proportion of severe patients was 34.91 ± 42.06% in the glucocorticoid group and 31.95 ± 37.61% in the no glucocorticoid group. To adjust those factors as further as possible between the glucocorticoid group and the no glucocorticoid group, studies with the mean age of 30–60 years and the proportion of patients with any comorbidities of 50% or less were selected. One hundred twelve patients treated with glucocorticoid were analyzed with a DSRP of 21.98 days (95% CIs 16.48–27.48 days, p < 0.01, I2 = 94%; Fig. 8c). One hundred twenty-two patients treated without glucocorticoid were analyzed with a DSRP of 16.14 days (95% CIs 12.60–19.68 days, p < 0.01, I2 = 92%; Fig. 8d). It was suggested that the usage of glucocorticoid had a mild effect on the DSRP.

Fig. 8
figure 8

Forest plot: a meta-analysis of the duration of SARS-CoV-2 RNA positivity in the upper respiratory tract samples based on the usage of glucocorticoid. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the patients treated with glucocorticoid (a) and the patients treated without glucocorticoid (b) was calculated using the random effects model. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the patients treated with glucocorticoid (c) and the patients treated without glucocorticoid (d) from the studies with the mean age of 30–60 years and the proportion of patients with any comorbidities of 50% or less was calculated using the random effects model. MRAW, the raw data of mean; 95%-CI, 95% confidence interval

Full size image

Subgroup analysis based on locality in the upper respiratory tract samples

In the upper respiratory tract samples, 8201 patients in Asian countries were analyzed with a DSRP of 18.10 days (95% CIs 16.95–19.25 days, p = 0, I2 = 98%; Fig. 9a). A total of 1434 patients in European countries were analyzed with a DSRP of 19.27 days (95% CIs 11.59–26.95 days, p = 0, I2 = 100%; Fig. 9b). The mean age was 48.61 ± 11.64 and 53.32 ± 9.54 years in Asian and European countries, respectively. The proportion of patients with any comorbidities was 42.74 ± 27.86% and 53.87 ± 17.26% in Asian and European countries, respectively. The proportion of severe patients was 33.75 ± 32.69% and 56.29 ± 41.72% in Asian and European countries, respectively. The proportion of patients treated with glucocorticoid was 28.09 ± 30.56% and 3.52 ± 9.31% in Asian and European countries, respectively. In studies from Asian countries, the patients were younger, the incidence of comorbidities was low, and COVID-19 was milder. However, glucocorticoid was used more in Asian countries. To adjust those factors as further as possible between Asian and European countries, studies with the mean age of 40 years or older and the proportion of severe patients of 40% or more were selected. Eight hundred thirty-one patients in Asian countries were analyzed with a DSRP of 20.66 days (95% CIs 18.18–23.14 days, p < 0.01, I2 = 96%; Fig. 9c). A total of 1268 patients in European countries were analyzed with a DSRP of 23.68 days (95% CIs 10.85–36.51 days, p < 0.01, I2 = 100%; Fig. 9d). It was suggested that the DSRP may be longer in patients in European countries.

Fig. 9
figure 9

Forest plot: subgroup meta-analysis of the duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the upper respiratory tract samples based on the locality. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the Asian countries (a) and European countries (b) was calculated using the random effects model. The duration of SARS-CoV-2 RNA positivity from the onset of COVID-19 in the Asian countries (c) and European countries (d) from the studies with the mean age of 40 years or older and the proportion of severe patients of 40% or more was calculated using the random effects model. MRAW, the raw data of mean; 95%-CI, 95% confidence interval

Full size image

Summary out results

The DSRP in various samples and various backgrounds are summarized in Fig. 10. An average of 18.29 days (95% CIs, 17.00–19.58 days) from the onset was required for the clearance of viral RNA from the upper respiratory tract samples. The DSRP on the sputum and the stool tended to be longer and that on the blood tended to be shorter. Due to analytical power, direct comparison showed that the DSRP was significantly longer than the upper respiratory tract samples in the stool alone.

Fig. 10
figure 10

The summary of the duration of SARS-CoV-2 RNA positivity in the various samples and the clinical characteristics

Full size image

The DSRP in the upper respiratory tract samples tended to be longer in patients older, with any comorbidities, severer, and treated with glucocorticoid, while it was not affected by gender and locality. The presence or absence of complications had the greatest impact on the difference in DSRP, although the effects of confounding factors cannot be ruled out.

Discussion

The DSRP in the sputum tended to be longer than that in the upper respiratory tract. In the early phase of COVID-19, the Ct value of the RT-PCR in the sputum tended to be lower than that in the upper respiratory tract [80, 98]. The high viral load of SARS-CoV-2 in the lungs may be one of the reasons for the long DSRP in the sputum. The shorter DSRP in the blood than that in the upper respiratory tract may be due to the lower viral load of SARS-CoV-2 in the blood in the early phase of COVID-19 [7, 72]. On the other hand, the viral load in the stool in the early phase of COVID-19 was not much different from that in the upper respiratory tract [6, 7]. SARS-CoV-2 may avoid elimination by unknown mechanisms and continue to replicate in the gastrointestinal tract [112].

The age of patients may affect the DSRP in the upper respiratory tract based on the sensitivity analysis and the subgroup analysis. No reports of differences in Ct values of RT-PCR between the older and younger groups were found, but the peak viral load in saliva exhibited a positive correlation with age [113]. Aging led to a delay or dysfunction in the initial triggering of the immune response [114]. In addition, older patients are likely to have other factors that prolong DSRP. For example, the older people are likely to have comorbidities than younger people. The age has been reported as one of the risk factors for severe COVID-19 [115], and the activity of daily living was associated with prognosis in older patients with COVID-19 [116]. Although the effects of confounding factors could not be ruled out in this analysis due to many missing data values, the information that DSRP tends to be longer in the older patients is considered clinically useful.

The presence of any comorbidities may affect the DSRP in the upper respiratory tract based on the sensitivity analysis and the subgroup analysis. Ct values of RT-PCR in the patients with comorbidities were lower [86]. Hypertension, cardiovascular diseases, diabetes, and obesity related to abnormal immune response [117]. The outcomes of COVID-19 are primarily influenced by comorbidities and particular disease states or treatments in patients with rheumatic diseases [118]. In this analysis, it was not possible to analyze which diseases had an impact on the DSRP, and the effects of confounding factors could not be ruled out. However, the difference in the DSRP was the largest in the comparison between the patients with any complications and the patients without any complications.

The severity of COVID-19 may affect the DSRP in the upper respiratory tract. The viral load of SARS-CoV-2 was possibly high in patients with critically severe COVID-19 [113]. The reduction of viral load correlated with the seroconversion in SARS [116] and the seroconversion was delayed in patients with severe COVID-19 [119]. It was reported that the period from the first confirmation of SARS-CoV-2 to the confirmation of clearance was 10 days in asymptomatic patients, which was shorter than 16 days in symptomatic patients [92]. In the subgroup analysis with a uniform patient background, the effect of severity on DSRP was mild, but the presence or absence of symptoms and severity definitely affect DSRP.

The usage of glucocorticoid may affect the DSRP in the upper respiratory tract. Initially, glucocorticoids were basically deprecated because they seemed to worsen viral clearance based on SARS [120]. As expected, DSRP tended to be longer in the patients treated with glucocorticoid in the subgroup analysis. However, the use of dexamethasone resulted in lower 28-day mortality among patients with severe COVID-19 [121]. Glucocorticoids should be used in severe patients because of delayed virus clearance.

Limitations

This study had several limitations. First, the positive result of the RT-PCR test does not always indicate the existence of transmittable SARS-CoV-2. Second, patients whose RT-PCR result for SARS-CoV-2 did not turn negative during the observation period were excluded. This study may underestimate the DSRP. Third, the funnel plots suggested the presence of bias or systemic heterogeneity. Fourth, the patient backgrounds in selected studies could not be fully unified. This may be a cause of the relatively high heterogeneity. It was difficult to reduce the heterogeneity enough with subgroup analyses. It may be possible to reduce heterogeneity if a more detailed patient background is available. Fifth, there were too many missing values. Multiple regression analysis could not be performed in the sensitivity analysis, and the number of patients was too small to further adjust the patient background in some subgroup analyses. In addition, it was not possible to assess which complications most affected the DSRP. Sixth, the effects of other drugs except for glucocorticoids on the DSRP could not be evaluated due to the small number of studies. Finally, the observational period of the included studies was until Jun 2020. The impact of SARS-CoV-2 variants, new therapies, and vaccinations on the DSRP could not be assessed.

Conclusion

We summarized the duration of SARS-CoV-2 RNA positivity from various specimens and clinical characteristics in patients with COVID-19. The DSRP in the upper respiratory tract samples was 18.29 days, and the DSRP in the sputum and stool samples tended to be longer. Age, comorbidity, severity, and usage of glucocorticoid possibly affected the DSRP. Our results provide the basic data for the natural course of COVID-19 and may be especially useful information for people at risk of severe COVID-19. In the future, the impact of vaccination against SARS-CoV-2 and the SARS-CoV-2 variants on the duration of RNA positivity and comparison between RT-PCR and other methods such as antigen test should be assessed.

Availability of data and materials

The data underlying this article are available in the article.

Abbreviations

CIs:

Confidence intervals

COVID-19:

Coronavirus disease 2019

Ct:

Threshold cycle

DSRP:

Duration of SARS-CoV-2 RNA positivity

RT-PCR:

Reverse transcription-polymerase chain reaction

RNA:

Ribonucleic acid

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

SD:

Standard deviation

References

  1. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020;382(13):1199–207. https://doi.org/10.1056/NEJMoa2001316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. CDC updates and shortens recommended isolation and quarantine period for general population. [cited 2022 February 14th]. Available from: https://www.cdc.gov/media/releases/2021/s1227-isolation-quarantine-guidance.html.

  3. Ministry of Health LaW. Clinical management of patients with COVID-19. A guide for front-line healthcare workers. Version 6.2. 2022.

    Google Scholar 

  4. Normile D. ‘Zero COVID’ is getting harder-but China is sticking with it. Science. 2021;374(6570):924. https://doi.org/10.1126/science.acx9657.

    Article  CAS  PubMed  Google Scholar 

  5. Huang Y, Chen S, Yang Z, Guan W, Liu D, Lin Z, et al. SARS-CoV-2 viral load in clinical samples from critically ill patients. Am J Respir Crit Care Med. 2020;201(11):1435–8. https://doi.org/10.1164/rccm.202003-0572LE.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wölfel R, Corman VM, Guggemos W, Seilmaier M, Zange S, Müller MA, et al. Virological assessment of hospitalized patients with COVID-2019. Nature. 2020;581(7809):465–9. https://doi.org/10.1038/s41586-020-2196-x.

    Article  CAS  PubMed  Google Scholar 

  7. 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. https://doi.org/10.1136/bmj.m1443.

    Article  PubMed  PubMed Central  Google Scholar 

  8. D'Ardes D, Pontolillo M, Esposito L, Masciarelli M, Boccatonda A, Rossi I, et al. Duration of COVID-19: data from an Italian Cohort and Potential Role for Steroids. Microorganisms. 2020;8(9). https://doi.org/10.3390/microorganisms8091327.

  9. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9, w264. https://doi.org/10.7326/0003-4819-151-4-200908180-00135.

    Article  PubMed  Google Scholar 

  10. Study Quality Assessment Tools. Available from: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools.

  11. Oxford Centre for Evidence-Based Medicine 2011. Available from: https://www.cebm.net/2016/05/ocebm-levels-of-evidence/.

  12. Leng Z, Zhu R, Hou W, Feng Y, Yang Y, Han Q, et al. Transplantation of ACE2(-) mesenchymal stem cells improves the outcome of patients with COVID-19 pneumonia. Aging Dis. 2020;11(2):216–28. https://doi.org/10.14336/AD.2020.0228.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135. https://doi.org/10.1186/1471-2288-14-135.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. https://doi.org/10.1136/bmj.327.7414.557.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48(3):452–8. https://doi.org/10.1038/bmt.2012.244.

    Article  CAS  PubMed  Google Scholar 

  16. Zhao J, Yuan Q, Wang H, Liu W, Liao X, Su Y, et al. Antibody responses to SARS-CoV-2 in patients with novel coronavirus disease 2019. Clin Infect Dis. 2020;71(16):2027–34. https://doi.org/10.1093/cid/ciaa344.

    Article  CAS  PubMed  Google Scholar 

  17. Xiao AT, Tong YX, Gao C, Zhu L, Zhang YJ, Zhang S. Dynamic profile of RT-PCR findings from 301 COVID-19 patients in Wuhan, China: a descriptive study. J Clin Virol. 2020;127:104346. https://doi.org/10.1016/j.jcv.2020.104346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Koblischke M, Traugott MT, Medits I, Spitzer FS, Zoufaly A, Weseslindtner L, et al. Dynamics of CD4 T cell and antibody responses in COVID-19 patients with different disease severity. Front Med (Lausanne). 2020;7:592629. https://doi.org/10.3389/fmed.2020.592629.

    Article  Google Scholar 

  19. Zheng T, Yang C, Wang HY, Chen X, Yu L, Wu ZL, et al. Clinical characteristics and outcomes of COVID-19 patients with gastrointestinal symptoms admitted to Jianghan Fangcang Shelter Hospital in Wuhan, China. J Med Virol. 2020;92(11):2735–41. https://doi.org/10.1002/jmv.26146.

    Article  CAS  PubMed  Google Scholar 

  20. Pongpirul WA, Wiboonchutikul S, Charoenpong L, Panitantum N, Vachiraphan A, Uttayamakul S, et al. Clinical course and potential predictive factors for pneumonia of adult patients with coronavirus disease 2019 (COVID-19): a retrospective observational analysis of 193 confirmed cases in Thailand. PLoS Negl Trop Dis. 2020;14(10):e0008806. https://doi.org/10.1371/journal.pntd.0008806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Song SH, Chen TL, Deng LP, Zhang YX, Mo PZ, Gao SC, et al. Clinical characteristics of four cancer patients with SARS-CoV-2 infection in Wuhan, China. Infect Dis Poverty. 2020;9(1):82. https://doi.org/10.1186/s40249-020-00707-1.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Weiskopf D, Schmitz KS, Raadsen MP, Grifoni A, Okba NMA, Endeman H, et al. Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome. Sci Immunol. 2020;5(48). https://doi.org/10.1126/sciimmunol.abd2071.

  23. Cao C, Chen M, He L, Xie J, Chen X. Clinical features and outcomes of COVID-19 patients with gastrointestinal symptoms. Crit Care. 2020;24(1):340. https://doi.org/10.1186/s13054-020-03034-x.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Liu Z, Ding L, Chen G, Zhao C, Luo X, Li X, et al. Clinical time features and chest imaging of 85 patients with COVID-19 in Zhuhai, China. Front Med (Lausanne). 2020;7:209. https://doi.org/10.3389/fmed.2020.00209.

    Article  Google Scholar 

  25. Fu HY, Luo Y, Gao JP, Wang L, Li HJ, Li X, et al. Effects of short-term low-dose glucocorticoids for patients with mild COVID-19. Biomed Res Int. 2020;2020:2854186.

    PubMed  PubMed Central  Google Scholar 

  26. Hu X, Hu C, Yang Y, Chen J, Zhong P, Wen Y, et al. Clinical characteristics and risk factors for severity of COVID-19 outside Wuhan: a double-center retrospective cohort study of 213 cases in Hunan, China. Ther Adv Respir Dis. 2020;14:1753466620963035. https://doi.org/10.1177/1753466620963035.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med. 2020;382(12):1177–9. https://doi.org/10.1056/NEJMc2001737.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. https://doi.org/10.1016/S0140-6736(20)30566-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Liu F, Xu A, Zhang Y, Xuan W, Yan T, Pan K, et al. Patients of COVID-19 may benefit from sustained lopinavir-combined regimen and the increase of eosinophil may predict the outcome of COVID-19 progression. Int J Infect Dis. 2020;95:183–91. https://doi.org/10.1016/j.ijid.2020.03.013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xu T, Chen C, Zhu Z, Cui M, Dai H, Xue Y. Clinical features and dynamics of viral load in imported and non-imported patients with COVID-19. Int J Infect Dis. 2020;94:68–71. https://doi.org/10.1016/j.ijid.2020.03.022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Gautret P, Lagier JC, Parola P, Hoang VT, Meddeb L, Mailhe M, et al. Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial. Int J Antimicrob Agents. 2020;105949.

  32. Seah IYJ, Anderson DE, Kang AEZ, Wang L, Rao P, Young BE, et al. Assessing viral shedding and infectivity of tears in coronavirus disease 2019 (COVID-19) patients. Ophthalmology. 2020;127(7):977–9. https://doi.org/10.1016/j.ophtha.2020.03.026.

    Article  PubMed  Google Scholar 

  33. Lescure FX, Bouadma L, Nguyen D, Parisey M, Wicky PH, Behillil S, et al. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis. 2020;20(6):697–706. https://doi.org/10.1016/S1473-3099(20)30200-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chen C, Gao G, Xu Y, Pu L, Wang Q, Wang L, et al. SARS-CoV-2-positive sputum and feces after conversion of pharyngeal samples in patients with COVID-19. Ann Intern Med. 2020;172(12):832–4. https://doi.org/10.7326/M20-0991.

    Article  PubMed  Google Scholar 

  35. Cheng CY, Lee YL, Chen CP, Lin YC, Liu CE, Liao CH, et al. Lopinavir/ritonavir did not shorten the duration of SARS CoV-2 shedding in patients with mild pneumonia in Taiwan. J Microbiol Immunol Infect. 2020;53(3):488–92. https://doi.org/10.1016/j.jmii.2020.03.032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kim ES, Chin BS, Kang CK, Kim NJ, Kang YM, Choi JP, et al. Clinical course and outcomes of patients with severe acute respiratory syndrome coronavirus 2 infection: a preliminary report of the first 28 patients from the Korean cohort study on COVID-19. J Korean Med Sci. 2020;35(13):e142. https://doi.org/10.3346/jkms.2020.35.e142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Duan K, Liu B, Li C, Zhang H, Yu T, Qu J, et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc Natl Acad Sci U S A. 2020;117(17):9490–6. https://doi.org/10.1073/pnas.2004168117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zha L, Li S, Pan L, Tefsen B, Li Y, French N, et al. Corticosteroid treatment of patients with coronavirus disease 2019 (COVID-19). Med J Aust. 2020;212(9):416–20. https://doi.org/10.5694/mja2.50577.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Cai Q, Huang D, Ou P, Yu H, Zhu Z, Xia Z, et al. COVID-19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy. 2020;75(7):1742–52. https://doi.org/10.1111/all.14309.

    Article  CAS  PubMed  Google Scholar 

  40. Young BE, Ong SWX, Kalimuddin S, Low JG, Tan SY, Loh J, et al. Epidemiologic features and clinical course of patients infected with SARS-CoV-2 in Singapore. JAMA. 2020;323(15):1488–94. https://doi.org/10.1001/jama.2020.3204.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lian N, Xie H, Lin S, Huang J, Zhao J, Lin Q. Umifenovir treatment is not associated with improved outcomes in patients with coronavirus disease 2019: a retrospective study. Clin Microbiol Infect. 2020;26(7):917–21. https://doi.org/10.1016/j.cmi.2020.04.026.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Qiancheng X, Jian S, Lingling P, Lei H, Xiaogan J, Weihua L, et al. COVID-19 sboAmtaWf. Coronavirus disease 2019 in pregnancy. Int J Infect Dis. 2020;95:376–83. https://doi.org/10.1016/j.ijid.2020.04.065.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shen C, Wang Z, Zhao F, Yang Y, Li J, Yuan J, et al. Treatment of 5 critically ill patients with COVID-19 with convalescent plasma. JAMA. 2020;323(16):1582–9. https://doi.org/10.1001/jama.2020.4783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dicker D, Kournos T, Marcoviciu D, Golan R. Do we know when to end isolation of persons affected with COVID-19? Eur J Intern Med. 2020;77:144–6. https://doi.org/10.1016/j.ejim.2020.04.063.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xiong Y, Song S, Ye G, Wang X. Family cluster of three recovered cases of pneumonia due to severe acute respiratory syndrome coronavirus 2 infection. BMJ Case Rep. 2020;13(5):e235302. https://doi.org/10.1136/bcr-2020-235302.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kim SE, Jeong HS, Yu Y, Shin SU, Kim S, Oh TH, et al. Viral kinetics of SARS-CoV-2 in asymptomatic carriers and presymptomatic patients. Int J Infect Dis. 2020;95:441–3. https://doi.org/10.1016/j.ijid.2020.04.083.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ling Y, Xu SB, Lin YX, Tian D, Zhu ZQ, Dai FH, et al. Persistence and clearance of viral RNA in 2019 novel coronavirus disease rehabilitation patients. Chin Med J. 2020;133(9):1039–43. https://doi.org/10.1097/CM9.0000000000000774.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wang B, Wang L, Kong X, Geng J, Xiao D, Ma C, et al. Long-term coexistence of SARS-CoV-2 with antibody response in COVID-19 patients. J Med Virol. 2020;92(9):1684–9. https://doi.org/10.1002/jmv.25946.

    Article  CAS  PubMed  Google Scholar 

  49. Qi L, Yang Y, Jiang D, Tu C, Wan L, Chen X, et al. Factors associated with the duration of viral shedding in adults with COVID-19 outside of Wuhan, China: a retrospective cohort study. Int J Infect Dis. 2020;96:531–7. https://doi.org/10.1016/j.ijid.2020.05.045.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zuo T, Zhang F, Lui GCY, Yeoh YK, Li AYL, Zhan H, et al. Alterations in gut microbiota of patients with COVID-19 during time of hospitalization. Gastroenterology. 2020;159:944–955.e948.

    Article  CAS  PubMed  Google Scholar 

  51. Wang H, Zhang Y, Mo P, Liu J, Wang F, Zhao Q. Neutrophil to CD4+ lymphocyte ratio as a potential biomarker in predicting virus negative conversion time in COVID-19. Int Immunopharmacol. 2020;85:106683. https://doi.org/10.1016/j.intimp.2020.106683.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Han J, Shi LX, Xie Y, Zhang YJ, Huang SP, Li JG, et al. Analysis of factors affecting the prognosis of COVID-19 patients and viral shedding duration. Epidemiol Infect. 2020;148:e125. https://doi.org/10.1017/S0950268820001399.

    Article  PubMed  Google Scholar 

  53. Yao Y, Chen W, Wu X, Shen L, Fu Y, Yang Q, et al. Clinical characteristics of COVID-19 patients in three consecutive generations of spread in Zhejiang, China. Clin Microbiol Infect. 2020;26(10):1380–5. https://doi.org/10.1016/j.cmi.2020.06.018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zuo Y, Liu Y, Zhong Q, Zhang K, Xu Y, Wang Z. Lopinavir/ritonavir and interferon combination therapy may help shorten the duration of viral shedding in patients with COVID-19: a retrospective study in two designated hospitals in Anhui, China. J Med Virol. 2020;92(11):2666–74. https://doi.org/10.1002/jmv.26127.

    Article  CAS  PubMed  Google Scholar 

  55. Chen X, Zhu B, Hong W, Zeng J, He X, Chen J, et al. Associations of clinical characteristics and treatment regimens with the duration of viral RNA shedding in patients with COVID-19. Int J Infect Dis. 2020;98:252–60. https://doi.org/10.1016/j.ijid.2020.06.091.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dubert M, Visseaux B, Isernia V, Bouadma L, Deconinck L, Patrier J, et al. Case report study of the first five COVID-19 patients treated with remdesivir in France. Int J Infect Dis. 2020;98:290–3. https://doi.org/10.1016/j.ijid.2020.06.093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lu Y, Li Y, Deng W, Liu M, He Y, Huang L, et al. Symptomatic infection is associated with prolonged duration of viral shedding in mild coronavirus disease 2019: a retrospective study of 110 children in Wuhan. Pediatr Infect Dis J. 2020;39(7):e95–9. https://doi.org/10.1097/INF.0000000000002729.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Fu Y, Han P, Zhu R, Bai T, Yi J, Zhao X, et al. Risk factors for viral RNA shedding in COVID-19 patients. Eur Respir J. 2020;56(1):2001190. https://doi.org/10.1183/13993003.01190-2020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yan D, Liu XY, Zhu YN, Huang L, Dan BT, Zhang GJ, et al. Factors associated with prolonged viral shedding and impact of lopinavir/ritonavir treatment in hospitalised non-critically ill patients with SARS-CoV-2 infection. Eur Respir J. 2020;56(1):2000799. https://doi.org/10.1183/13993003.00799-2020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hu Z, Lv Y, Xu C, Sun W, Chen W, Peng Z, et al. Clinical use of short-course and low-dose corticosteroids in patients with non-severe COVID-19 during pneumonia progression. Front Public Health. 2020;8:355. https://doi.org/10.3389/fpubh.2020.00355.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Yu C, Zhang Z, Guo Y, Shi J, Pei G, Yao Y, et al. Lopinavir/ritonavir is associated with pneumonia resolution in COVID-19 patients with influenza coinfection: a retrospective matched-pair cohort study. J Med Virol. 2020;93(1):472–80. https://doi.org/10.1002/jmv.26260.

    Article  CAS  PubMed  Google Scholar 

  62. Guo X, Jie Y, Ye Y, Chen P, Li X, Gao Z, et al. Upper respiratory tract viral ribonucleic acid load at hospital admission is associated with coronavirus disease 2019 disease severity. Open Forum Infect Dis. 2020;7:ofaa282.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Wang G, Yu N, Xiao W, Zhao C, Wang Z. Consecutive false-negative rRT-PCR test results for SARS-CoV-2 in patients after clinical recovery from COVID-19. J Med Virol. 2020;92(11):2887–90. https://doi.org/10.1002/jmv.26192.

    Article  CAS  PubMed  Google Scholar 

  64. Li TZ, Cao ZH, Chen Y, Cai MT, Zhang LY, Xu H, et al. Duration of SARS-CoV-2 RNA shedding and factors associated with prolonged viral shedding in patients with COVID-19. J Med Virol. 2020;93:506–12.

    Article  PubMed  Google Scholar 

  65. Lin L, Luo S, Qin R, Yang M, Wang X, Yang Q, et al. Long-term infection of SARS-CoV-2 changed the body’s immune status. Clin Immunol. 2020;218:108524. https://doi.org/10.1016/j.clim.2020.108524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Yu T, Tian C, Chu S, Zhou H, Zhang Z, Luo S, et al. COVID-19 patients benefit from early antiviral treatment: a comparative, retrospective study. J Med Virol. 2020;92(11):2675–83. https://doi.org/10.1002/jmv.26129.

    Article  CAS  PubMed  Google Scholar 

  67. Warabi Y, Tobisawa S, Kawazoe T, Murayama A, Norioka R, Morishima R, et al. Effects of oral care on prolonged viral shedding in coronavirus disease 2019 (COVID-19). Spec Care Dentist. 2020;40(5):470–4. https://doi.org/10.1111/scd.12498.

    Article  PubMed  Google Scholar 

  68. Otsubo S, Aoyama Y, Kinoshita K, Goto T, Otsubo Y, Kamano D, et al. Prolonged shedding of SARS-CoV-2 in COVID-19 infected hemodialysis patients. Ther Apher Dial. 2020;25(3):356–8. https://doi.org/10.1111/1744-9987.13566.

    Article  CAS  PubMed  Google Scholar 

  69. Lee PH, Tay WC, Sutjipto S, Fong SW, Ong SWX, Wei WE, et al. Associations of viral ribonucleic acid (RNA) shedding patterns with clinical illness and immune responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Clin Transl Immunol. 2020;9(7):e1160. https://doi.org/10.1002/cti2.1160.

    Article  CAS  Google Scholar 

  70. Xu K, Chen Y, Yuan J, Yi P, Ding C, Wu W, et al. Factors associated with prolonged viral RNA shedding in patients with coronavirus disease 2019 (COVID-19). Clin Infect Dis. 2020;71(15):799–806. https://doi.org/10.1093/cid/ciaa351.

    Article  CAS  PubMed  Google Scholar 

  71. Danis K, Epaulard O, Bénet T, Gaymard A, Campoy S, Bothelo-Nevers E, et al. Cluster of coronavirus disease 2019 (Covid-19) in the French Alps, 2020. Clin Infect Dis. 2020;71(15):825–32. https://doi.org/10.1093/cid/ciaa424.

    Article  CAS  PubMed  Google Scholar 

  72. Xu D, Zhou F, Sun W, Chen L, Lan L, Li H, et al. Relationship between serum SARS-CoV-2 nucleic acid (RNAemia) and organ damage in COVID-19 patients: a cohort study. Clin Infect Dis. 2020;73(1):68–75. https://doi.org/10.1093/cid/ciaa1085.

    Article  CAS  Google Scholar 

  73. Lopez A, Duclos G, Pastene B, Bezulier K, Guilhaumou R, Solas C, et al. Effects of hydroxychloroquine on Covid-19 in intensive care unit patients: preliminary results. Int J Antimicrob Agents. 2020;56(5):106136. https://doi.org/10.1016/j.ijantimicag.2020.106136.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Ma Y, Zeng H, Zhan Z, Lu H, Zeng Z, He C, et al. Corticosteroid use in the treatment of COVID-19: a multicenter retrospective study in Hunan, China. Front Pharmacol. 2020;11:1198. https://doi.org/10.3389/fphar.2020.01198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hu F, Yin G, Chen Y, Song J, Ye M, Liu J, et al. Corticosteroid, oseltamivir and delayed admission are independent risk factors for prolonged viral shedding in patients with coronavirus disease 2019. Clin Respir J. 2020;14(11):1067–75. https://doi.org/10.1111/crj.13243.

    Article  CAS  PubMed  Google Scholar 

  76. Liu F, Ji C, Luo J, Wu W, Zhang J, Zhong Z, et al. Clinical characteristics and corticosteroids application of different clinical types in patients with corona virus disease 2019. Sci Rep. 2020;10(1):13689. https://doi.org/10.1038/s41598-020-70387-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shi D, Wu W, Wang Q, Xu K, Xie J, Wu J, et al. Clinical characteristics and factors associated with long-term viral excretion in patients with severe acute respiratory syndrome coronavirus 2 infection: a single-center 28-day study. J Infect Dis. 2020;222(6):910–8. https://doi.org/10.1093/infdis/jiaa388.

    Article  CAS  PubMed  Google Scholar 

  78. Gong Y, Guan L, Jin Z, Chen S, Xiang G, Gao B. Effects of methylprednisolone use on viral genomic nucleic acid negative conversion and CT imaging lesion absorption in COVID-19 patients under 50 years old. J Med Virol. 2020;92(11):2551–5. https://doi.org/10.1002/jmv.26052.

    Article  CAS  PubMed  Google Scholar 

  79. Wu Y, Chen W, Li W, Zhao M, Wei Q, Zhang X, et al. Clinical characteristics, therapeutic management, and prognostic factors of adult COVID-19 inpatients with hematological malignancies. Leuk Lymphoma. 2020;61(14):3440–50. https://doi.org/10.1080/10428194.2020.1808204.

    Article  CAS  PubMed  Google Scholar 

  80. Han M, Xu M, Zhang Y, Liu Z, Li S, He T, et al. Assessing SARS-CoV-2 RNA levels and lymphocyte/T cell counts in COVID-19 patients revealed initial immune status as a major determinant of disease severity. Med Microbiol Immunol. 2020;209(6):657–68. https://doi.org/10.1007/s00430-020-00693-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Buetti N, Trimboli P, Mazzuchelli T, Lo Priore E, Balmelli C, Trkola A, et al. Diabetes mellitus is a risk factor for prolonged SARS-CoV-2 viral shedding in lower respiratory tract samples of critically ill patients. Endocrine. 2020;70(3):454–60. https://doi.org/10.1007/s12020-020-02465-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Soh TV, Dzawani M, Noorlina N, Nik F, Norazmi A. Clinical characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) patients in Hospital Tengku Ampuan Afzan. Med J Malaysia. 2020;75(5):479–84.

    CAS  PubMed  Google Scholar 

  83. Mancuso P, Venturelli F, Vicentini M, Perilli C, Larosa E, Bisaccia E, et al. Temporal profile and determinants of viral shedding and of viral clearance confirmation on nasopharyngeal swabs from SARS-CoV-2-positive subjects: a population-based prospective cohort study in Reggio Emilia, Italy. BMJ Open. 2020;10(8):e040380. https://doi.org/10.1136/bmjopen-2020-040380.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Li Q, Li W, Jin Y, Xu W, Huang C, Li L, et al. Efficacy evaluation of early, low-dose, short-term corticosteroids in adults hospitalized with non-severe COVID-19 pneumonia: a retrospective cohort study. Infect Dis Ther. 2020;9(4):823–36. https://doi.org/10.1007/s40121-020-00332-3.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Mori H, Obinata H, Murakami W, Tatsuya K, Sasaki H, Miyake Y, et al. Comparison of COVID-19 disease between young and elderly patients: hidden viral shedding of COVID-19. J Infect Chemother. 2021;27(1):70–5. https://doi.org/10.1016/j.jiac.2020.09.003.

    Article  CAS  PubMed  Google Scholar 

  86. Asai N, Sakanashi D, Ohashi W, Nakamura A, Yamada A, Kawamoto Y, et al. Could threshold cycle value correctly reflect the severity of novel coronavirus disease 2019 (COVID-19)? J Infect Chemother. 2021;27(1):117–9. https://doi.org/10.1016/j.jiac.2020.09.010.

    Article  CAS  PubMed  Google Scholar 

  87. Zheng X, Chen J, Deng L, Fang Z, Chen G, Ye D, et al. Risk factors for the COVID-19 severity and its correlation with viral shedding: a retrospective cohort study. J Med Virol. 2021;93(2):952–61. https://doi.org/10.1002/jmv.26367.

    Article  CAS  PubMed  Google Scholar 

  88. Li X, Chan JF, Li KK, Tso EY, Yip CC, Sridhar S, et al. Detection of SARS-CoV-2 in conjunctival secretions from patients without ocular symptoms. Infection. 2020;49(2):257–65. https://doi.org/10.1007/s15010-020-01524-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Liu W, Liu Y, Xu Z, Jiang T, Kang Y, Zhu G, et al. Clinical characteristics and predictors of the duration of SARS-CoV-2 viral shedding in 140 healthcare workers. J Intern Med. 2020;288(6):725–36. https://doi.org/10.1111/joim.13160.

    Article  CAS  PubMed  Google Scholar 

  90. Jeong TH, Pak C, Ock M, Lee SH, Son JS, Jeon YJ. Real asymptomatic SARS-CoV-2 infection might be rare: importance of careful interviews and follow-up. J Korean Med Sci. 2020;35(37):e333. https://doi.org/10.3346/jkms.2020.35.e333.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Romão VC, Oliveira-Ramos F, Cruz-Machado AR, Martins P, Barreira S, Silva-Dinis J, et al. A COVID-19 outbreak in a rheumatology department upon the early days of the pandemic. Front Med (Lausanne). 2020;7:576162.

    Google Scholar 

  92. Jiang C, Wang Y, Hu M, Wen L, Wen C, Zhu W, et al. Antibody seroconversion in asymptomatic and symptomatic patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clin Transl Immunol. 2020;9(9):e1182. https://doi.org/10.1002/cti2.1182.

    Article  CAS  Google Scholar 

  93. Ding C, Feng X, Chen Y, Yuan J, Yi P, Li Y, et al. Effect of corticosteroid therapy on the duration of SARS-CoV-2 clearance in patients with mild COVID-19: a retrospective cohort study. Infect Dis Ther. 2020;9(4):943–52. https://doi.org/10.1007/s40121-020-00337-y.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Li L, Liang Y, Hu F, Yan H, Li Y, Xie Z, et al. Molecular and serological characterization of SARS-CoV-2 infection among COVID-19 patients. Virology. 2020;551:26–35. https://doi.org/10.1016/j.virol.2020.09.008.

    Article  CAS  PubMed  Google Scholar 

  95. Wang Y, Zhang L, Sang L, Ye F, Ruan S, Zhong B, et al. Kinetics of viral load and antibody response in relation to COVID-19 severity. J Clin Invest. 2020;130(10):5235–44. https://doi.org/10.1172/JCI138759.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wu B, Lei ZY, Wu KL, He JR, Cao HJ, Fu J, et al. Compare the epidemiological and clinical features of imported and local COVID-19 cases in Hainan, China. Infect Dis Poverty. 2020;9(1):143. https://doi.org/10.1186/s40249-020-00755-7.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Zhou S, Yang Y, Zhang X, Li Z, Liu X, Hu C, et al. Clinical course of 195 critically ill COVID-19 patients: a retrospective multicenter study. Shock. 2020;54(5):644–51. https://doi.org/10.1097/SHK.0000000000001629.

    Article  CAS  PubMed  Google Scholar 

  98. Yang Y, Yang M, Yuan J, Wang F, Wang Z, Li J, et al. Comparative sensitivity of different respiratory specimen types for molecular diagnosis and monitoring of SARS-CoV-2 shedding. Innovation (N Y). 2020;1:100061.

    Google Scholar 

  99. Wu C, Hou D, Du C, Cai Y, Zheng J, Xu J, et al. Corticosteroid therapy for coronavirus disease 2019-related acute respiratory distress syndrome: a cohort study with propensity score analysis. Crit Care. 2020;24(1):643. https://doi.org/10.1186/s13054-020-03340-4.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Zhou B, She J, Wang Y, Ma X. Duration of viral shedding of discharged patients with severe COVID-19. Clin Infect Dis. 2020;71(16):2240–2. https://doi.org/10.1093/cid/ciaa451.

    Article  CAS  PubMed  Google Scholar 

  101. Zhou C, Zhang T, Ren H, Sun S, Yu X, Sheng J, et al. Impact of age on duration of viral RNA shedding in patients with COVID-19. Aging (Albany NY). 2020;12(22):22399–404. https://doi.org/10.18632/aging.104114.

    Article  CAS  Google Scholar 

  102. Nakamura S, Kanemasa Y, Atsuta Y, Fujiwara S, Tanaka M, Fukushima K, et al. Characteristics and outcomes of coronavirus disease 2019 (COVID-19) patients with cancer: a single-center retrospective observational study in Tokyo, Japan. Int J Clin Oncol. 2020;26(3):485–93. https://doi.org/10.1007/s10147-020-01837-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Wang C, Zhou L, Chen J, Yang Y, Huang T, Fu M, et al. The differences of clinical characteristics and outcomes between imported and local patients of COVID-19 in Hunan: a two-center retrospective study. Respir Res. 2020;21(1):313. https://doi.org/10.1186/s12931-020-01551-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kashiwagi K, Maeda T, Yoshizawa S, Sato T, Aoki K, Ishii Y, et al. Comparison of IgG antibodies, SARS-CoV-2 load, and prognostic indicators in patients with severe and mild COVID-19 in Japan. J Nippon Med Sch. 2020;88(4):380–3. https://doi.org/10.1272/jnms.JNMS.2021_88-417.

    Article  PubMed  Google Scholar 

  105. Yongchen Z, Shen H, Wang X, Shi X, Li Y, Yan J, et al. Different longitudinal patterns of nucleic acid and serology testing results based on disease severity of COVID-19 patients. Emerg Microbes Infect. 2020;9(1):833–6. https://doi.org/10.1080/22221751.2020.1756699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Liu P, Cai J, Jia R, Xia S, Wang X, Cao L, et al. Dynamic surveillance of SARS-CoV-2 shedding and neutralizing antibody in children with COVID-19. Emerg Microbes Infect. 2020;9(1):1254–8. https://doi.org/10.1080/22221751.2020.1772677.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Shen Y, Zheng F, Sun D, Ling Y, Chen J, Li F, et al. Epidemiology and clinical course of COVID-19 in Shanghai, China. Emerg Microbes Infect. 2020;9(1):1537–45. https://doi.org/10.1080/22221751.2020.1787103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Hu H, Du H, Li J, Wang Y, Wu X, Wang C, et al. Early prediction and identification for severe patients during the pandemic of COVID-19: a severe COVID-19 risk model constructed by multivariate logistic regression analysis. J Glob Health. 2020;10(2):020510. https://doi.org/10.7189/jogh.10.020510.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Agusti A, Guillen E, Ayora A, Anton A, Aguilera C, Vidal X, et al. Efficacy and safety of hydroxychloroquine in healthcare professionals with mild SARS-CoV-2 infection: prospective, non-randomized trial. Enferm Infecc Microbiol Clin. 2020;S0213-005X:30413–4.

    Google Scholar 

  110. Zhang X, Lu S, Li H, Wang Y, Lu Z, Liu Z, et al. Viral and antibody kinetics of COVID-19 patients with different disease severities in acute and convalescent phases: a 6-month follow-up study. Virol Sin. 2020;35(6):820–9. https://doi.org/10.1007/s12250-020-00329-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Feng Z, Zhao H, Kang W, Liu Q, Wu J, Bragazzi NL, et al. Association of paraspinal muscle measurements on chest computed tomography with clinical outcomes in patients with severe coronavirus disease 2019. J Gerontol A Biol Sci Med Sci. 2020;76(3):e78–84. https://doi.org/10.1093/gerona/glaa317.

    Article  CAS  Google Scholar 

  112. Qian Q, Fan L, Liu W, Li J, Yue J, Wang M, et al. Direct evidence of active SARS-CoV-2 replication in the intestine. Clin Infect Dis. 2021;73(3):361–6. https://doi.org/10.1093/cid/ciaa925.

    Article  CAS  PubMed  Google Scholar 

  113. Liu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci. 2020;63(3):364–74. https://doi.org/10.1007/s11427-020-1643-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Bartleson JM, Radenkovic D, Covarrubias AJ, Furman D, Winer DA, Verdin E. SARS-CoV-2, COVID-19 and the ageing immune system. Nat Aging. 2021;1(9):769–82. https://doi.org/10.1038/s43587-021-00114-7.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Chen X, Zhao B, Qu Y, Chen Y, Xiong J, Feng Y, et al. Detectable serum SARS-CoV-2 viral load (RNAaemia) is closely correlated with drastically elevated interleukin 6 (IL-6) level in critically ill COVID-19 patients. Clin Infect Dis. 2020;71(8):1937–42. https://doi.org/10.1093/cid/ciaa449.

    Article  CAS  PubMed  Google Scholar 

  116. Peiris JS, Chu CM, Cheng VC, Chan KS, Hung IF, Poon LL, et al. Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet. 2003;361(9371):1767–72. https://doi.org/10.1016/S0140-6736(03)13412-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Matsuyama T, Yoshinaga SK, Shibue K, Mak TW. Comorbidity-associated glutamine deficiency is a predisposition to severe COVID-19. Cell Death Differ. 2021;28(12):3199–213. https://doi.org/10.1038/s41418-021-00892-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Grainger R, AHJ K, Conway R, Yazdany J, Robinson PC. COVID-19 in people with rheumatic diseases: risks, outcomes, treatment considerations. Nat Rev Rheumatol. 2022:1–14.

  119. Shen L, Wang C, Zhao J, Tang X, Shen Y, Lu M, et al. Delayed specific IgM antibody responses observed among COVID-19 patients with severe progression. Emerg Microbes Infect. 2020;9(1):1096–101. https://doi.org/10.1080/22221751.2020.1766382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Russell CD, Millar JE, Baillie JK. Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury. Lancet. 2020;395(10223):473–5. https://doi.org/10.1016/S0140-6736(20)30317-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, et al. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2020;384:693–704.

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank our colleagues from Osaka University Center of Medical Data Science and Advanced Clinical Epidemiology Investigator’s Research Project for their providing insight and expertise for our research and Enago (www.enago.jp) for the English language review.

Funding

This work was supported by research grants from the Center of Innovation program (COISTREAM) from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) (to A.K.); the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant no. JP18H05282 to A.K. and grant no. JP21K16287 to T.M.); the Japan Agency for Medical Research and Development (AMED) (grant nos. J200705023, J200705710, J200705049, JP18cm016335, and JP18cm059042 to A.K.); a grant from the Kansai Economic Federation (KANKEIREN) (to A.K.); and grants from Mitsubishi Zaidan1 (to A.K.).

Author information

Authors and Affiliations

  1. Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan

    Yasutaka Okita, Takayoshi Morita & Atsushi Kumanogoh

  2. Department of Immunopathology, World Premier International Immunology Frontier Research Center (iFReC), Suita, Osaka, Japan

    Atsushi Kumanogoh

  3. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan

    Atsushi Kumanogoh

  4. Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Osaka, Japan

    Atsushi Kumanogoh

Authors
  1. Yasutaka Okita
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takayoshi Morita
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atsushi Kumanogoh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.O. and T.M. designed the study and carried out the literature search. Y.O. and T.M. independently acquired data, screened records, extracted data, assessed risk of bias, and performed the statistical analyses. All authors wrote and revised the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Takayoshi Morita.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Okita, Y., Morita, T. & Kumanogoh, A. Duration of SARS-CoV-2 RNA positivity from various specimens and clinical characteristics in patients with COVID-19: a systematic review and meta-analysis. Inflamm Regener 42, 16 (2022). https://doi.org/10.1186/s41232-022-00205-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41232-022-00205-x

Keywords

  • COVID-19
  • SARS-CoV-2
  • SARS-CoV-2 RNA positivity
  • Viral shedding
  • Coronavirus
  • Meta-analysis
  • Systematic review

Inflammation and Regeneration

ISSN: 1880-8190

Contact us