Decision letter: SARS-CoV-2 shedding dynamics across the respiratory tract, sex, and disease severity for adult and pediatric COVID-19

Abstract

Article Figures and data Abstract Introduction Materials and methods Results Discussion Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Previously, we conducted a systematic review and analyzed the respiratory kinetics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Chen et al., 2021). How age, sex, and coronavirus disease 2019 (COVID-19) severity interplay to influence the shedding dynamics of SARS-CoV-2, however, remains poorly understood. Methods: We updated our systematic dataset, collected individual case characteristics, and conducted stratified analyses of SARS-CoV-2 shedding dynamics in the upper (URT) and lower respiratory tract (LRT) across COVID-19 severity, sex, and age groups (aged 0–17 years, 18–59 years, and 60 years or older). Results: The systematic dataset included 1266 adults and 136 children with COVID-19. Our analyses indicated that high, persistent LRT shedding of SARS-CoV-2 characterized severe COVID-19 in adults. Severe cases tended to show slightly higher URT shedding post-symptom onset, but similar rates of viral clearance, when compared to nonsevere infections. After stratifying for disease severity, sex and age (including child vs. adult) were not predictive of respiratory shedding. The estimated accuracy for using LRT shedding as a prognostic indicator for COVID-19 severity was up to 81%, whereas it was up to 65% for URT shedding. Conclusions: Virological factors, especially in the LRT, facilitate the pathogenesis of severe COVID-19. Disease severity, rather than sex or age, predicts SARS-CoV-2 kinetics. LRT viral load may prognosticate COVID-19 severity in patients before the timing of deterioration and should do so more accurately than URT viral load. Funding: Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, NSERC Senior Industrial Research Chair, and the Toronto COVID-19 Action Fund. Introduction As of August 8, 2021, the coronavirus disease 2019 (COVID-19) pandemic has caused more than 202.6 million infections and 4.2 million deaths globally (Dong et al., 2020). The clinical spectrum of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is wide, ranging from asymptomatic infection to fatal disease. For cases that deteriorate into severe COVID-19, deterioration occurs, on median, 10 days after symptom onset (Solomon et al., 2020; Zhou et al., 2020). Risk factors for severe illness and death include age, sex, smoking, and comorbidities, such as obesity, hypertension, diabetes, and cardiovascular disease (Onder et al., 2020; Tartof et al., 2020; Zhou et al., 2020). Emerging evidence indicates that age and sex differences in innate, cross-reactive, and adaptive immunity facilitate the higher risks observed in older and male cases (Rydyznski Moderbacher et al., 2020; Ng et al., 2020; Pierce et al., 2020; Takahashi et al., 2020). Conversely, robust immune responses putatively mediate nonsevere illness, in part, by controlling the replication of SARS-CoV-2 (Lucas et al., 2021; Lucas et al., 2020). As a respiratory virus, the shedding dynamics of SARS-CoV-2 in the upper (URT) and lower respiratory tract (LRT) provide insight into the clinical and epidemiological characteristics of COVID-19. URT viral load has been associated with transmission risk, duration of infectiousness, disease severity, and mortality (Chen et al., 2021b; Fu et al., 2021; Magleby et al., 2020; Marks et al., 2021; Pujadas et al., 2020; van Kampen et al., 2021; Westblade et al., 2020; Wölfel et al., 2020). Key questions, however, remain. While chest computed tomography (CT) evidence of viral pneumonitis suggests pulmonary replication in most symptomatic cases (Bernheim et al., 2020), the LRT kinetics of SARS-CoV-2, especially as related to disease severity, remain unclear. How age, sex, and disease severity influence shedding dynamics is poorly understood, especially for children. Moreover, it is unclear whether respiratory viral load can accurately predict COVID-19 severity, with conflicting results from analyses of low sample numbers (Argyropoulos et al., 2020; Lucas et al., 2020; Pujadas et al., 2020; Silva et al., 2021; Walsh et al., 2020; Westblade et al., 2020). For insight into these questions, we conducted a systematic review on SARS-CoV-2 quantitation from respiratory specimens and developed a large, diverse dataset of viral loads and individual case characteristics. Stratified analyses then assessed SARS-CoV-2 shedding dynamics across the respiratory tract, age, sex, and COVID-19 severity. Materials and methods Data sources and searches Request a detailed protocol Our systematic review identified studies reporting SARS-CoV-2 quantitation in respiratory specimens taken during the estimated infectious period (−3 to 10 days from symptom onset [DFSO]) (He et al., 2020; Wölfel et al., 2020). The systematic review protocol was based on our previous study (Chen et al., 2021a) and was prospectively registered on PROSPERO (registration number, CRD42020204637). The systematic review was conducted according to Cochrane methods guidance (Higgins et al., 2019). PRISMA reporting guidelines were followed (Moher et al., 2009). Up to November 20, 2020, we searched, without the use of filters or language restrictions, the following sources: MEDLINE (Ovid, 1946 to November 20, 2020, Wyllie et al., 2020), EMBASE (Ovid, 1974 to November 20, 2020, Wyllie et al., 2020), Cochrane Central Register of Controlled Trials (via Ovid, 1991 to November 20, 2020, Wyllie et al., 2020), Web of Science Core Collection (up to November 20, 2020, Wyllie et al., 2020), and medRxiv and bioRxiv (both searched through Google Scholar via the Publish or Perish program, up to November 20, 2020, Wyllie et al., 2020). We also gathered studies by searching through the reference lists of review articles identified by the database search, by searching through the reference lists of included articles, through expert recommendation (by Eric J Topol and Akiko Iwasaki on Twitter) and by hand-searching through journals. A comprehensive search was developed by a librarian (ZP). The line-by-line search strategies for all databases are included in Figure 1—source data 1 to 5. The search results were exported from each database and uploaded to the Covidence online system (research resource identifier, RRID:SCR_016484) for deduplication and screening. Study selection Request a detailed protocol Studies that reported SARS-CoV-2 quantitation in individual URT (nasopharyngeal swab [NPS], nasopharyngeal aspirate, oropharyngeal swab [OPS], or posterior oropharyngeal saliva [POS]) or LRT (endotracheal aspirate [ETA] or sputum [Spu]) specimens taken during the estimated infectious period (−3 to 10 DFSO) in humans were included (additional details given in the Appendix). As semiquantitative metrics (cycle threshold [Ct] values) cannot be compared on an absolute scale between studies based on instrument and batch variation (Han et al., 2021), studies reporting specimen measurements as Ct values, without quantitative calibration, were excluded. Two authors (PZC and NB) independently screened titles and abstracts and reviewed full texts. At the full-text stage, reference lists were reviewed for study inclusion. Inconsistencies were resolved by discussion and consensus, and 26 studies met the inclusion criteria (Bal et al., 2020; Benotmane et al., 2020; Biguenet et al., 2021; Fajnzylber et al., 2020; Han et al., 2020; Hirotsu et al., 2020; Hurst et al., 2020; Iwasaki et al., 2020; L’Huillier et al., 2020; Lavezzo et al., 2020; Pan et al., 2020; Peng et al., 2020; Shrestha et al., 2020; Sun et al., 2020; To et al., 2020; van Kampen et al., 2021; Vetter et al., 2020; Wölfel et al., 2020; Wyllie et al., 2020; Xu et al., 2020a; Yazdanpanah, 2021; Yilmaz et al., 2021; Yonker et al., 2020; Zhang et al., 2021; Zheng et al., 2020; Zou et al., 2020). Additional details on study selection can be found in our previous protocol (Chen et al., 2021a). Data extraction and risk-of-bias assessment Request a detailed protocol Two authors (PZC and NB) independently collected data (specimen measurements taken between –3 and 10 DFSO, specimen type, volume of viral transport media [VTM], and case characteristics, including age, sex, and disease severity) from contributing studies and assessed risk of bias using a modified version of the Joanna Briggs Institute (JBI) tools for case series, analytical cross-sectional studies, and prevalence studies (Moola et al., 2020; Munn et al., 2019; Munn et al., 2015) (shown in the Appendix). Items were judged with responses to data inquiries, if authors responded. Data were collected for individually reported specimens of known type, with known DFSO, and for COVID-19 cases with known age, sex or severity. Case characteristics were collected directly from contributing studies when reported individually or obtained via data request from the authors. Data from serially sampled asymptomatic cases were included, and the day of laboratory diagnosis was referenced as 0 DFSO (Lavezzo et al., 2020; Wölfel et al., 2020). Based on the modified JBI checklist, studies were considered to have low risk of bias if they met the majority of items and included item 1 (representative sample). Discrepancies were resolved by discussion and consensus. Studies at high or unclear risk of bias typically included samples that were not representative of the target population; did not report the VTM volume used; had non-consecutive inclusion for case series and cohort studies or did not use probability-based sampling for cross-sectional studies; and did not report the response rate. Respiratory viral load Request a detailed protocol To enable analyses based on respiratory viral load (rVL, viral RNA concentration in the respiratory tract) and to account for between-study variation in specimen measurements, the rVL for each collected sample was estimated based on the specimen concentration (viral RNA concentration in the specimen) and its dilution factor in VTM. Typically, swabbed specimens (NPS and OPS) report the viral RNA concentration in VTM. Based on the VTM volume reported in the study along with the expected uptake volume for swabs (0.128 ± 0.031 ml, mean ± SD) (Warnke et al., 2014), we calculated the dilution factor for each respiratory specimen and then estimated the rVL. Similarly, liquid specimens (ETA, POS, and Spu) are often diluted in VTM, and the rVL was estimated based on the reported collection and VTM volumes. If the diluent volume was not reported, then VTM volumes of 1 ml (NPS and OPS) or 2 ml (POS and ETA) were assumed (Lavezzo et al., 2020; To et al., 2020). Unless dilution was reported, Spu specimens were taken as undiluted (Wölfel et al., 2020). The non-reporting of VTM volume was noted as an element increasing risk of bias in the modified JBI critical appraisal checklist. For laboratory-confirmed COVID-19 cases, negative specimen measurements were taken at the reported assay detection limit in the respective study. Case definitions Request a detailed protocol As severity in the clinical manifestations of COVID-19 and case-fatality rates tend to increase among children (aged 0–17 years), younger adults (aged 18–59 years), and older adults (aged 60 years or older) (Onder et al., 2020; Zheng et al., 2020), the data were delineated by these three age groups. Cases were also categorized by sex. U.S. National Institutes of Health guidance was used to categorize disease severity as nonsevere or severe (National Institutes of Health, 2021) (Appendix 1—table 1). The nonsevere group included those with asymptomatic infection (individuals who test positive via a molecular test for SARS-CoV-2 and report no symptoms consistent with COVID-19); mild illness (individuals who report any signs or symptoms of COVID-19, including fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell, but who do not have dyspnea or abnormal chest imaging); and moderate illness (individuals with clinical or radiographic evidence of LRT disease, fever >39.4°C or SpO2 >94% on room air) disease. The severe group included those with severe illness (individuals who have SpO2 <94% on room air, [PaO2/FiO2] < 300 mmHg, respiratory rate >30 breaths/min or lung infiltrates > 50%) and critical illness (respiratory failure, septic shock, or multiple organ dysfunction). Regression analyses Request a detailed protocol To assess the respiratory shedding of SARS-CoV-2 and compare age, sex, or severity groups, we analyzed the data via normal linear regression (Hurst et al., 2020; Lucas et al., 2020). Previous studies have shown that SARS-CoV-2 shedding tends to diminish exponentially after 1 DFSO in the URT and, at least, after 4 DFSO in the LRT (Bernheim et al., 2020; Chen et al., 2021a; Wölfel et al., 2020). Although LRT shedding may peak before 4 DFSO, there is limited data near or before symptom onset. Hence, rVLs (in units of log10 copies/ml) between 1 and 10 DFSO for the URT, or 4 and 10 DFSO for the LRT, were fitted using linear regression with interaction: (1) V=α+β1X1+β2X2+β3X1X2, where V represents the rVL, α represents the estimated mean rVL (at 1 DFSO for URT or 4 DFSO for LRT) for the reference group, X1 represents DFSO for the reference group, X2 represents the comparison group, β1 represents the effect of DFSO on rVL for the reference group, β2 represents the effect of the comparison group on the main effect (mean rVL at 1 DFSO), and β3 represents the interaction between DFSO and groups. Regression analyses were offset by DFSO such that mean rVLs at 1 DFSO for URT, or 4 DFSO for LRT, were compared between groups by the main effect (i.e., effect on the intercept in the regression t-test for β2). Shedding dynamics were compared between groups by interaction (regression t-test for β3). The statistical significance of viral clearance for each group was analyzed using simple linear regression (regression t-test on the slope). Each group in statistical analyses included all rVLs for which the relevant characteristic (LRT or URT, age group, sex, or disease severity) was ascertained at the individual level. Groups with small sample sizes were not compared, as these analyses are more sensitive to potential sampling error. Regression models were extrapolated to 0 log10 copies/ml to estimate the total duration of shedding. Some clinical studies report shedding duration based on assay negativity, when the viral RNA concentration in the specimen reaches the detection limit of the assay (often between 1 and 4 log10 copies/ml), and these cases may continue to shed viral RNA. To show the relationship between the two approaches, we used our regression model for URT shedding and estimated the shedding duration to a specimen concentration of 3 log10 copies/ml when sampling was conducted with nasopharyngeal swabs (approximately equivalent to an rVL of 2.1 log10 copies/ml). Then, the estimated mean duration of URT shedding for severe cases was 20.8 (95% CI: 14.5–27.0) DFSO, while it was 20.3 (95% CI: 16.8–23.7) DFSO for nonsevere cases. These values are in line with those reported by studies considering the assay detection limit (Cevik et al., 2021), supporting our regression models, and can be compared with those reported in the body text. Statistical analyses were performed using OriginPro 2019b (RRID:SCR_014212, OriginLab) and the General Linear regression app. p-Values below 0.05 were considered statistically significant. Distribution analyses Request a detailed protocol Previously, our analyses found that SARS-CoV-2 rVLs best conform to Weibull distributions (Chen et al., 2021b). To assess heterogeneity in shedding in this study, rVL data were fitted to Weibull distributions. The Weibull quantile function and Weibull cumulative distribution function were used to estimate the rVL at a case percentile and the percentage of cases at a given rVL, respectively. Each distribution was fitted to groups that included all rVLs for which the relevant characteristic (LRT or URT, age group, sex, or disease severity) was ascertained at the individual level. Distribution fitting was performed using Matlab R2019b (RRID:SCR_001622, MathWorks) and the Distribution Fitter app. Prognostication accuracy Request a detailed protocol The fitted Weibull distributions were used to estimate the accuracy when using URT or LRT rVLs of SARS-CoV-2 as a prognostic indicator for COVID-19 severity. The overlapped area under the curve (AUC) and separated AUC were calculated using the rVL distributions for severe and nonsevere adult COVID-19. These calculations were performed for each DFSO and, separately, for the URT and LRT. The estimated maximal accuracy for prognostication at a given rVL threshold was then estimated by A=50+50*AUCseparated , where AUCseparated represents the AUC that was separated for the nonsevere and severe distributions. The 95% CIs for prognostication accuracy were estimated using the proportional 95% CIs in the respective Weibull cumulative distributions. As the Weibull cumulative distributions estimate the percentage of cases at a given rVL, they were also used to estimate the sensitivity and specificity at a given prognostic threshold of rVL. The cases with rVL lower than the prognostic threshold were predicted to have nonsevere COVID-19, whereas those with rVL above it were predicted to have severe COVID-19. Hence, we used the cumulative distributions for nonsevere and severe adult cases on a DFSO and calculated the proportion of cases that were true positive, false positive, false negative, and true negative rates across prognostic thresholds of rVL. Sensitivity and specificity were calculated based on these values. These analyses were coded in Matlab R2019b (RRID:SCR_001622, MathWorks) and are available at GitHub (copy archived at swh:1:rev:c96390f98f47f17939f3669c7c8fad96f9603e84, Chen, 2019). Results Overview of contributing studies The systematic search (Figure 1—source data 1, Figure 1—source data 2, Figure 1—source data 3, Figure 1—source data 4, Figure 1—source data 5) identified 5802 deduplicated results. After screening and full-text review, 26 studies met the inclusion criteria, and data were collected for individually reported specimens of known type and taken on a known DFSO for COVID-19 cases with known age, sex, or severity (Figure 1). From 1402 COVID-19 cases, we collected 1915 quantitative specimen measurements (viral RNA concentration in a respiratory specimen) of SARS-CoV-2 (Table 1) and used them to estimate rVLs (viral RNA concentration in the respiratory tract) (Figure 1—figure supplement 1). For pediatric cases, the search found only nonsevere infections and URT specimen measurements. Appendix 1—table 1 and Appendix 1—table 2 summarize the characteristics of contributing studies, of which 18 had low risk of bias according to the modified JBI critical appraisal checklist. Figure 1 with 1 supplement see all Download asset Open asset Study selection. Figure 1—source data 1 Search strategy used for MEDLINE. https://cdn.elifesciences.org/articles/70458/elife-70458-fig1-data1-v2.docx Download elife-70458-fig1-data1-v2.docx Figure 1—source data 2 Search strategy used for EMBASE. https://cdn.elifesciences.org/articles/70458/elife-70458-fig1-data2-v2.docx Download elife-70458-fig1-data2-v2.docx Figure 1—source data 3 Search strategy used for Cochrane Central. https://cdn.elifesciences.org/articles/70458/elife-70458-fig1-data3-v2.docx Download elife-70458-fig1-data3-v2.docx Figure 1—source data 4 Search strategy used for Web of Science Core Collection. https://cdn.elifesciences.org/articles/70458/elife-70458-fig1-data4-v2.docx Download elife-70458-fig1-data4-v2.docx Figure 1—source data 5 Search strategy used for medRxiv and bioRxiv. https://cdn.elifesciences.org/articles/70458/elife-70458-fig1-data5-v2.docx Download elife-70458-fig1-data5-v2.docx Table 1 Characteristics of adult and pediatric coronavirus disease 2019 (COVID-19) cases in the systematic dataset. AdultPediatricCases, n1266136URT specimens, n1513192LRT specimens, n2100Mean age (SD), years51.8 (18.0)8.7 (5.3)Male, n (%)528 (44.0)63 (52.5)Disease severity, n (%)Asymptomatic2 (0.2)5 (3.7)Mild710 (57.5)112 (83.6)Moderate178 (14.4)17 (12.7)Severe167 (13.5)0 (0.0)Critical178 (14.4)0 (0.0) Adult cases were those aged 18 years or older, while pediatric cases were those aged younger than 18 years. Upper respiratory tract = URT. Lower respiratory tract = LRT. URT shedding of SARS-CoV-2 for adult COVID-19 To interpret the complex interplay between SARS-CoV-2 shedding dynamics and age, sex, and COVID-19 severity, we stratified our systematic dataset into age, sex, and severity groups and then conducted a series of linear regression analyses. For adult COVID-19, regression analysis showed that the mean URT rVL at 1 DFSO was significantly greater (p = 0.005) for severely infected cases (8.28 [95% CI: 7.71–8.84] log10 copies/ml) than nonsevere ones (7.45 [95% CI: 7.26–7.65] log10 copies/ml) (Figure 2A, D,). Meanwhile, these groups showed comparable URT dynamics post-symptom onset (p for interaction = 0.479), as severe adult cases tended to cleared SARS-CoV-2 from the URT at –0.31 (95% CI: –0.40 to–0.22) log10 copies/ml/day while nonsevere ones did so at –0.28 (95% CI: –0.32 to –0.24) log10 copies/ml/day (Figure 2A, E). For severe cases, the estimated mean duration of URT shedding (down to 0 log10 copies/ml) was 27.5 (95% CI: 21.2–33.8) DFSO; it was 27.9 (95% CI: 24.4–31.3) DFSO for nonsevere cases. Figure 2 with 2 supplements see all Download asset Open asset Comparison of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding in the upper respiratory tract (URT) across severity, sex, and age groups. (A–C) URT shedding for severe and nonsevere adult (aged 18 years or older) coronavirus disease 2019 (COVID-19) (A), for nonsevere pediatric (aged 0–17 years) and nonsevere adult COVID-19 (B) and for nonsevere pediatric and severe adult COVID-19 (C). Open circles represent respiratory viral load (rVL) data and were offset from their day from symptom onset (DFSO) for visualization. Lines and bands show regressions and their 95% CIs, respectively. (D and E) Comparisons of URT shedding levels at 1 DFSO (D) and URT shedding dynamics (E) between severity, age, and sex groups for COVID-19. (F and G) Comparisons of URT shedding levels at 1 DFSO (F) and URT shedding dynamics (G) between pediatric and adult groups for COVID-19. The black line in (E) and (G) depicts 0, the threshold for no significant trend in SARS-CoV-2 clearance. Linear regression analyses with interaction determined p-values and compared shedding levels and dynamics between the two groups in each row. After stratifying adults for disease severity, our analyses showed no significant differences in URT shedding levels or dynamics between sex or age groups (Figure 2D,E, Figure 2—figure supplement 1). For severe disease, male and female cases had comparable mean rVLs at 1 DFSO (p = 0.326) and rates of SARS-CoV-2 clearance (p for interaction = 0.280). Similarly, for nonsevere illness, male and female cases had no significant difference in mean rVL at 1 DFSO (p = 0.085) or URT dynamics (p for interaction = 0.644). For nonsevere illness, younger and older adults had no significant difference in URT shedding levels at 1 DFSO (p = 0.294) or post-symptom onset dynamics (p for interaction = 0.100). For severe disease, the adult age groups showed similar mean rVLs at 1 DFSO (p = 0.915) and rates of viral clearance (p for interaction = 0.359). URT shedding of SARS-CoV-2 for pediatric COVID-19 For pediatric COVID-19, regression estimated that, in the URT, the mean rVL at 1 DFSO was 7.32 (95% CI: 6.78–7.86) log10 copies/ml and the SARS-CoV-2 clearance rate was –0.32 (95% CI: –0.42 to –0.22) log10 copies/ml/day (Figure 2F,G). Both estimates were comparable between the sexes for children (Figure 2—figure supplement 1). The estimated mean duration of URT shedding (down to 0 log10 copies/ml) was 22.6 (95% CI: 17.0–28.1) DFSO for children with COVID-19. Between pediatric cases, who had nonsevere illness in our dataset, and adults with nonsevere illness, both URT shedding at 1 DFSO (p = 0.653) and URT dynamics (p for interaction = 0.400) were similar (Figure 2B, F and G). In contrast, URT shedding at 1 DFSO was greater for severely affected adults when compared to children with nonsevere disease (p = 0.017), but URT dynamics remained similar (p for interaction = 0.863) (Figure 2C, F and G). LRT shedding of SARS-CoV-2 for adult COVID-19 For adults, our analyses showed that high, persistent LRT shedding of SARS-CoV-2 was associated with severe COVID-19 but not nonsevere illness (Figure 3A). At the initial day in our analyzed period (4 DFSO), the mean rVL in the LRT of severe cases (8.42 [95% CI: 7.67–9.17] log10 copies/ml) was significantly greater (p = 0.006) than that of nonsevere cases (6.82 [95% CI: 5.95–7.69] log10 copies/ml) (Figure 3D). Between severities, the difference in LRT clearance rates was marginally above the threshold for statistical significance (p for interaction = 0.053). Nonetheless, severe cases had persistent LRT shedding, with no significant trend in SARS-CoV-2 clearance up to 10 DFSO (–0.14 [95% CI: –0.32–0.030] log10 copies/ml/day, p = 0.105), whereas nonsevere cases rapidly cleared the virus from the LRT (–0.41 [95% CI: –0.64 to –0.19] log10 copies/ml/day, p < 0.001) (Figure 3E). For nonsevere cases, the estimated mean duration of LRT shedding (down to 0 log10 copies/ml) was 20.4 (95% CI: 13.2–27.7) DFSO. Figure 3 with 1 supplement see all Download asset Open asset Comparison of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding in the lower respiratory tract (LRT) across severity and age groups and the upper respiratory tract (URT). (A–C). Shedding in the LRT for severe and nonsevere adult (aged 18 years or older) COVID-19 (A), in the LRT and URT nonsevere adult COVID-19 (B) and in the LRT and URT severe adult COVID-19 (C). Open circles represent respiratory viral load (rVL) data and were offset from their day from symptom onset (DFSO) for visualization. Lines and bands show regressions and their 95% CIs, respectively. (D and E) Comparisons of shedding levels at 4 DFSO (D) and URT shedding dynamics (E) between severity and age groups in the LRT and between the LRT and URT. The black line in (E) depicts 0, the threshold for no significant trend in SARS-CoV-2 clearance. Linear regression analyses with interaction determined p-values and compared shedding levels and dynamics between the two groups in each row. For severe COVID-19, regression analysis showed comparable mean LRT rVLs at 4 DFSO between younger and older adults (p = 0.745) (Figure 3D). For severe cases, both age groups also had persistent LRT shedding in the analyzed period: younger adults (–0.20 [95% CI: –0.32 to 0.042] log10 copies/ml/day, p = 0.105) and older adults (–0.13 [95% CI: –0.39 to 0.13] log10 copies/ml/day, p = 0.316) both had no significant trend in SARS-CoV-2 clearance (Figure 3E, Figure 3—figure supplement 1A). Likewise, severely affected male cases had no significant trend in LRT shedding (0.001 [95% CI: –0.16 to 0.19] log10 copies/ml/day, p = 0.988) (Figure 3—figure supplement 1B). The female group included few samples, and statistically analyses were not conducted (Appendix 1—table 3). Interestingly, nonsevere cases showed similar SARS-CoV-2 shedding between the URT and LRT (Figure 3B), whereas severe cases shed greater and longer in the LRT than in the URT (Figure 3C). At 4 DFSO, the URT rVL of nonsevere adults was 6.62 (95% CI: 6.50–6.74) log10 copies/ml, which was not different from the LRT rVL of nonsevere adults (p = 0.651). Conversely, for severe adults, the rVL at 4 DFSO was significantly lower in the URT (7.34 [95% CI: 7.01–7.68] log10 copies/ml) than the LRT (p = 0.031) (Figure 3D). Heterogeneity in URT shedding of SARS-CoV-2 While regression analyses compared mean shedding levels and dynamics, we fitted rVLs to Weibull distributions to assess the heterogeneity in rVL. Both severe and nonsevere adult COVID-19 showed broad variation in URT shedding throughout disease course (Figure 4A). For severe disease, the standard deviation (SD) of rVL was 1.86, 2.34, 1.89, and 1.90 log10 copies/ml at 2, 4, 7, and 10 DFSO, respectively. For nonsevere illness, these SDs were 2.08, 1.90, 1.89, and 1.96 log10 copies/ml, respectively. Notably, our distribution analyses indicated that the top 2–9% of adults with COVID-19 harbored 80% of the SARS-CoV-2 copies in the URT on each DFSO (Figure 4—figure supplement 1A-D). Figure 4 with 3 supplements see all Download asset Open asset Heterogeneity in, and severity prognostication from, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding for adult coronavirus disease 2019 (COVID-19). (A–D) Upper respiratory tract (URT) analyses. (A) Estimated distributions at 2, 4, 7, and 10 days from symptom onset (DFSO) of URT shedding for adults (aged 18 years or older) with nonsevere or severe COVID-19. (B) Overlapped or separated areas under the curve for the distributions in (A). (C) Estimated accuracy for using URT shedding of SARS-CoV-2 as a prognostic indicator for COVID-19 severity. (D) Cumulative distributions of URT shedding for adults with nonsevere or severe COVID-19 at various DFSO. (E–H) Lower respiratory tract (LRT) analyses. (E) Estimated distributions at 5, 6, 8, and 10 DFSO of LRT shedding for adults with nonsevere or severe COVID-19. (F) Over