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      Viral Dynamics of SARS-CoV-2 Variants in Vaccinated and Unvaccinated Persons

      letter
      , Ph.D. , Ph.D. , Ph.D., , Ph.D. , Ph.D., , Ph.D. , Ph.D. , M.D., M.P.H. , Ph.D., , M.D., , Ph.D., , Ph.D., , Ph.D., , Ph.D., , Ph.D., , Ph.D. , M.D. , Ph.D. , M.D., Ph.D.
      The New England Journal of Medicine
      Massachusetts Medical Society
      Keyword part (code): 18Keyword part (keyword): Infectious DiseaseKeyword part (code): 18_2Keyword part (keyword): VaccinesKeyword part (code): 18_12Keyword part (keyword): Coronavirus , 18, Infectious Disease, Keyword part (code): 18_2Keyword part (keyword): VaccinesKeyword part (code): 18_12Keyword part (keyword): Coronavirus , 18_2, Vaccines, 18_12, Coronavirus

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          Abstract

          To the Editor: Two opposing forces that are shaping the coronavirus disease 2019 (Covid-19) pandemic are the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern and the uptake of vaccines. Measurement of SARS-CoV-2 viral load over the course of acute infection can inform hypotheses about the mechanisms that underlie variation in transmissibility according to variant and vaccination status. 1 Recent evidence suggests that infections with the delta variant feature higher peak viral loads than those in other lineages 2 and that vaccine recipients who are infected with SARS-CoV-2 may clear the infection more quickly than unvaccinated persons. 3 However, descriptions of SARS-CoV-2 viral dynamics have been principally based on cross-sectional studies in which testing was triggered by the onset of symptoms. Such study designs overlook viral dynamics during the early stages of infection and introduce bias in viral load measurements from different periods of the pandemic. 4 To overcome these limitations, we collected and analyzed a prospective, longitudinal set of 19,941 SARS-CoV-2 viral samples obtained from 173 participants as part of the occupational health program of the National Basketball Association between November 28, 2020, and August 11, 2021. (Details regarding the characteristics of the population are provided in Table S1 in the Supplementary Appendix, available with the full text of this letter at NEJM.org.) Using a Bayesian hierarchical statistical model, 5 we compared SARS-CoV-2 viral dynamics among 36 participants who were infected with the B.1.1.7 (alpha) variant, 36 participants with the B.1.617.2 (delta) variant, and 41 participants with a variant that was not of current interest or concern, along with 37 vaccinated and 136 unvaccinated participants. We found no meaningful difference in the mean peak viral load (with a lower peak cycle threshold [Ct] indicating a higher viral load), proliferation duration, clearance duration, or duration of acute infection of either the alpha or the delta variant as compared with variants not of interest or concern, as evidenced by overlapping 95% credible intervals (Figure 1A, 1B, and 1C, Table S2, and Fig. S1). We also found no meaningful difference in the mean peak viral load or proliferation duration between vaccinated and unvaccinated participants (Figure 1D and 1E, Table S2, and Fig. S2). A lower peak Ct was slightly more frequent in infections with the delta variant than in those with the alpha variant or variants not of interest or concern: 13.0% of the posterior delta trajectories had a Ct count of less than 15 (9.6 log10 RNA copies per milliliter), as compared with 6.9% for the alpha variant and 10.2% for variants not of interest or concern (Figure 1F and Fig. S1G). It is unclear whether this finding reflects a biologic characteristic of the delta variant, the limited number of cases, the higher proportion of delta infections among vaccine recipients, or other factors. Breakthrough infections among vaccine recipients were characterized by a faster clearance time than that among unvaccinated participants, with a mean of 5.5 days (95% credible interval, 4.6 to 6.5) and 7.5 days (95% credible interval, 6.8 to 8.2), respectively. The shorter clearance time led to a shorter overall duration of infection among vaccine recipients (Figure 1G). Our ability to detect differences in SARS-CoV-2 viral dynamics was limited by the high degree of interpersonal variation among our study participants, as well as the small sample size, which also prevented us from subcategorizing the population further according to variant and vaccination status. The participants in this study were predominantly healthy young men and thus were not representative of the general population. Symptoms were not systematically tracked, nor did we test for the presence of infectious virus. This study provides data on acute SARS-CoV-2 viral dynamics for some variants of concern among vaccinated and unvaccinated persons. Additional data regarding prospective, longitudinal testing among diverse cohorts are needed to better understand differences in SARS-CoV-2 viral trajectories and inform interventions to mitigate the effects of Covid-19.

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          Virological and serological kinetics of SARS-CoV-2 Delta variant vaccine-breakthrough infections: a multi-center cohort study

          Objectives Highly effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed but variants of concerns are worrisome, especially B.1.617.2 (Delta) which has rapidly spread across the world. We aim to study if vaccination alters virological and serological kinetics in breakthrough infections. Methods We conducted a multi-centre retrospective cohort study of patients in Singapore who had received a licensed mRNA vaccine and been admitted to hospital with B.1.617.2 SARS-CoV-2 infection. We compared clinical features, virological and serological kinetics (anti-nucleocapsid, anti-spike and surrogate virus neutralization titres) between fully vaccinated and unvaccinated individuals. Results Out of 218 individuals with B.1.617.2 infection, 84 received a mRNA vaccine of which 71 were fully vaccinated, 130 were unvaccinated and 4 received a non-mRNA. Despite significantly older age in the vaccine-breakthrough group, only 2.8% (2/71) developed severe COVID-19 requiring oxygen supplementation compared to 53.1% (69/130) in the unvaccinated group (p<0.001). Odds of severe COVID-19 following vaccination were significantly lower (adjusted odds ratio 0.07 95%CI: 0.015-0.335, p=0.001). PCR cycle threshold values were similar between vaccinated and unvaccinated groups at diagnosis, but viral loads decreased faster in vaccinated individuals. Early, robust boosting of anti-spike protein antibodies was observed in vaccinated patients, however, these titers were significantly lower against B.1.617.2 as compared with wildtype vaccine strain. Conclusion The mRNA vaccines are highly effective at preventing symptomatic and severe COVID-19 associated with B.1.617.2 infection. Vaccination is associated with faster decline in viral RNA load and a robust serological response. Vaccination remains a key strategy for control of COVID-19 pandemic.
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            Viral dynamics of acute SARS-CoV-2 infection and applications to diagnostic and public health strategies

            SARS-CoV-2 infections are characterized by viral proliferation and clearance phases and can be followed by low-level persistent viral RNA shedding. The dynamics of viral RNA concentration, particularly in the early stages of infection, can inform clinical measures and interventions such as test-based screening. We used prospective longitudinal quantitative reverse transcription PCR testing to measure the viral RNA trajectories for 68 individuals during the resumption of the 2019–2020 National Basketball Association season. For 46 individuals with acute infections, we inferred the peak viral concentration and the duration of the viral proliferation and clearance phases. According to our mathematical model, we found that viral RNA concentrations peaked an average of 3.3 days (95% credible interval [CI] 2.5, 4.2) after first possible detectability at a cycle threshold value of 22.3 (95% CI 20.5, 23.9). The viral clearance phase lasted longer for symptomatic individuals (10.9 days [95% CI 7.9, 14.4]) than for asymptomatic individuals (7.8 days [95% CI 6.1, 9.7]). A second test within 2 days after an initial positive PCR test substantially improves certainty about a patient’s infection stage. The effective sensitivity of a test intended to identify infectious individuals declines substantially with test turnaround time. These findings indicate that SARS-CoV-2 viral concentrations peak rapidly regardless of symptoms. Sequential tests can help reveal a patient’s progress through infection stages. Frequent, rapid-turnaround testing is needed to effectively screen individuals before they become infectious. Viral dynamics of SARS-CoV-2 infections can inform public health surveillance strategies and clinical care, but the full infection dynamics have remained undescribed. This study presents such data from the National Baseball Association 2019-20 season restart and demonstrates their applications.
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              Estimating epidemiologic dynamics from cross-sectional viral load distributions

              INTRODUCTION: Current approaches to epidemic monitoring rely on case counts, test positivity rates, and reported deaths or hospitalizations. These metrics, however, provide a limited and often biased picture as a result of testing constraints, unrepresentative sampling, and reporting delays. Random cross-sectional virologic surveys can overcome some of these biases by providing snapshots of infection prevalence but currently offer little information on the epidemic trajectory without sampling across multiple time points. RATIONALE: We develop a new method that uses information inherent in cycle threshold (Ct) values from reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests to robustly estimate the epidemic trajectory from multiple or even a single cross section of positive samples. Ct values are related to viral loads, which depend on the time since infection; Ct values are generally lower when the time between infection and sample collection is short. Despite variation across individuals, samples, and testing platforms, Ct values provide a probabilistic measure of time since infection. We find that the distribution of Ct values across positive specimens at a single time point reflects the epidemic trajectory: A growing epidemic will necessarily have a high proportion of recently infected individuals with high viral loads, whereas a declining epidemic will have more individuals with older infections and thus lower viral loads. Because of these changing proportions, the epidemic trajectory or growth rate should be inferable from the distribution of Ct values collected in a single cross section, and multiple successive cross sections should enable identification of the longer-term incidence curve. Moreover, understanding the relationship between sample viral loads and epidemic dynamics provides additional insights into why viral loads from surveillance testing may appear higher for emerging viruses or variants and lower for out-breaks that are slowing, even absent changes in individual-level viral kinetics. RESULTS: Using a mathematical model for population-level viral load distributions calibrated to known features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load kinetics, we show that the median and skewness of Ct values in a random sample change over the course of an epidemic. By formalizing this relationship, we demonstrate that Ct values from a single random cross section of virologic testing can estimate the time-varying reproductive number of the virus in a population, which we validate using data collected from comprehensive SARS-CoV-2 testing in long-term care facilities. Using a more flexible approach to modeling infection incidence, we also develop a method that can reliably estimate the epidemic trajectory in even more-complex populations, where interventions may be implemented and relaxed over time. This method performed well in estimating the epidemic trajectory in the state of Massachusetts using routine hospital admissions RT-qPCR testing data—accurately replicating estimates from other sources for the entire state. CONCLUSION: This work provides a new method for estimating the epidemic growth rate and a framework for robust epidemic monitoring using RT-qPCR Ct values that are often simply discarded. By deploying single or repeated (but small) random surveillance samples and making the best use of the semiquantitative testing data, we can estimate epidemic trajectories in real time and avoid biases arising from nonrandom samples or changes in testing practices over time. Understanding the relationship between population-level viral loads and the state of an epidemic reveals important implications and opportunities for interpreting virologic surveillance data. It also highlights the need for such surveillance, as these results show how to use it most informatively. Ct values reflect the epidemic trajectory and can be used to estimate incidence. ( A and B ) Whether an epidemic has rising or falling incidence will be reflected in the distribution of times since infection (A), which in turn affects the distribution of Ct values in a surveillance sample (B). ( C ) These values can be used to assess whether the epidemic is rising or falling and estimate the incidence curve. Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.
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                Author and article information

                Journal
                N Engl J Med
                N Engl J Med
                nejm
                The New England Journal of Medicine
                Massachusetts Medical Society
                0028-4793
                1533-4406
                01 December 2021
                01 December 2021
                : NEJMc2102507
                Affiliations
                Harvard T.H. Chan School of Public Health, Boston, MA skissler@ 123456hsph.harvard.edu
                Yale School of Public Health, New Haven, CT
                IQVIA, Durham, NC
                Yale School of Public Health, New Haven, CT
                IQVIA, Durham, NC
                Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC
                TEMPUS Labs, Chicago, IL
                Columbia University Vagelos College of Physicians and Surgeons, New York, NY
                Yale School of Public Health, New Haven, CT
                Harvard T.H. Chan School of Public Health, Boston, MA
                Author notes

                Drs. Kissler, Fauver, and Mack and Drs. Grubaugh and Grad contributed equally to this letter.

                Author information
                http://orcid.org/0000-0001-6000-8387
                http://orcid.org/0000-0002-0399-8238
                http://orcid.org/0000-0001-5646-1314
                Article
                NJ202112013852605
                10.1056/NEJMc2102507
                8693673
                34941024
                dd3d0dc0-23ff-4966-9765-8319c693b3d3
                Copyright © 2021 Massachusetts Medical Society. All rights reserved.

                This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections.

                History
                Funding
                Funded by: National Basketball Association, and the National Basketball Players Association, FundRef ;
                Funded by: Huffman Family Donor Advised Fund, FundRef ;
                Funded by: Emergent Ventures at the Mercatus Center, FundRef ;
                Funded by: Morris-Singer Fund, FundRef ;
                Categories
                Correspondence
                Custom metadata
                2021-12-01T17:00:00-05:00
                2021
                12
                01
                17
                00
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                -05:00

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