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1. Trends in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seroprevalence in Massachusetts Estimated from Newborn Screening Specimens

   https://doi.org/10.1093/cid/ciac158  

   Ma, Kevin C. ; Hale, Jaime E. ; Grad, Yonatan H. ; Alter, Galit ; Luzuriaga, Katherine ; Eaton, Roger B. ; Fischinger, Stephanie ; Kaur, Devinder ; Brody, Robin ; Siddiqui, Sameed M. ; et al ( February 2022 , Clinical Infectious Diseases)

   Estimating the cumulative incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for setting public health policies. We leveraged deidentified Massachusetts newborn screening specimens as an accessible, retrospective source of maternal antibodies for estimating statewide seroprevalence in a nontest-seeking population.

   We analyzed 72 117 newborn specimens collected from November 2019 through December 2020, representing 337 towns and cities across Massachusetts. Seroprevalence was estimated for the Massachusetts population after correcting for imperfect test specificity and nonrepresentative sampling using Bayesian multilevel regression and poststratification.

   Statewide seroprevalence was estimated to be 0.03% (90% credible interval [CI], 0.00–0.11) in November 2019 and rose to 1.47% (90% CI: 1.00–2.13) by May 2020, following sustained SARS-CoV-2 transmission in the spring. Seroprevalence plateaued from May onward, reaching 2.15% (90% CI: 1.56–2.98) in December 2020. Seroprevalence varied substantially by community and was particularly associated with community percent non-Hispanic Black (β = .024; 90% CI: 0.004–0.044); i.e., a 10% increase in community percent non-Hispanic Black was associated with 27% higher odds of seropositivity. Seroprevalence estimates had good concordance with reported case counts and wastewater surveillance for most of 2020, prior to the resurgence of transmission in winter.

   Cumulative incidence of SARS-CoV-2 protective antibody in Massachusetts was low as of December 2020, indicating that a substantial fraction of the population was still susceptible. Maternal seroprevalence data from newborn screening can inform longitudinal trends and identify cities and towns at highest risk, particularly in settings where widespread diagnostic testing is unavailable.

    

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2. Direct wastewater extraction as a simple and effective method for SARS-CoV-2 surveillance and COVID-19 community-level monitoring

   https://doi.org/10.1093/femsmc/xtad004  

   Lott, Megan E. J. ; Norfolk, William A. ; Dailey, Cody A. ; Foley, Amelia M. ; Melendez-Declet, Carolina ; Robertson, Megan J. ; Rathbun, Stephen L. ; Lipp, Erin K. ( January 2023 , FEMS Microbes)

   Wastewater surveillance has proven to be an effective tool to monitor the transmission and emergence of infectious agents at a community scale. Workflows for wastewater surveillance generally rely on concentration steps to increase the probability of detection of low-abundance targets, but preconcentration can substantially increase the time and cost of analyses while also introducing additional loss of target during processing. To address some of these issues, we conducted a longitudinal study implementing a simplified workflow for SARS-CoV-2 detection from wastewater, using a direct column-based extraction approach. Composite influent wastewater samples were collected weekly for 1 year between June 2020 and June 2021 in Athens-Clarke County, Georgia, USA. Bypassing any concentration step, low volumes (280 µl) of influent wastewater were extracted using a commercial kit, and immediately analyzed by RT-qPCR for the SARS-CoV-2 N1 and N2 gene targets. SARS-CoV-2 viral RNA was detected in 76% (193/254) of influent samples, and the recovery of the surrogate bovine coronavirus was 42% (IQR: 28%, 59%). N1 and N2 assay positivity, viral concentration, and flow-adjusted daily viral load correlated significantly with per-capita case reports of COVID-19 at the county-level (ρ = 0.69–0.82). To compensate for the method’s high limit of detection (approximately 106–107 copies l−1 in wastewater), we extracted multiple small-volume replicates of each wastewater sample. With this approach, we detected as few as five cases of COVID-19 per 100 000 individuals. These results indicate that a direct-extraction-based workflow for SARS-CoV-2 wastewater surveillance can provide informative and actionable results.

    

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3. Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

   https://doi.org/10.1371/journal.pmed.1003660  

   Iwanami, Shoya ; Ejima, Keisuke ; Kim, Kwang Su ; Noshita, Koji ; Fujita, Yasuhisa ; Miyazaki, Taiga ; Kohno, Shigeru ; Miyazaki, Yoshitsugu ; Morimoto, Shimpei ; Nakaoka, Shinji ; et al ( July 2021 , PLOS Medicine)

   Kretzschmar, Mirjam E. (Ed.)

   Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates ( p -value < 0.001). The mean decay rates were 1.17 d −1 (95% CI: 1.06 to 1.27 d −1 ), 0.777 d −1 (0.716 to 0.838 d −1 ), and 0.450 d −1 (0.378 to 0.522 d −1 ) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model. 

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4. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment

   https://doi.org/10.1186/s40168-021-01083-0  

   Marotz, Clarisse ; Belda-Ferre, Pedro ; Ali, Farhana ; Das, Promi ; Huang, Shi ; Cantrell, Kalen ; Jiang, Lingjing ; Martino, Cameron ; Diner, Rachel E. ; Rahman, Gibraan ; et al ( June 2021 , Microbiome)

   SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.

   We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.

   Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genusRothiastrongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.

   These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment.

    

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5. Effect of SARS-CoV-2 digital droplet RT-PCR assay sensitivity on COVID-19 wastewater based epidemiology

   https://doi.org/10.1371/journal.pwat.0000066  

   Kim, Sooyeol ; Wolfe, Marlene K. ; Criddle, Craig S. ; Duong, Dorothea H. ; Chan-Herur, Vikram ; White, Bradley J. ; Boehm, Alexandria B. ( November 2022 , PLOS Water)

   Santos, Ricardo (Ed.)

   We developed and implemented a framework for examining how molecular assay sensitivity for a viral RNA genome target affects its utility for wastewater-based epidemiology. We applied this framework to digital droplet RT-PCR measurements of SARS-CoV-2 and Pepper Mild Mottle Virus genes in wastewater. Measurements were made using 10 replicate wells which allowed for high assay sensitivity, and therefore enabled detection of SARS-CoV-2 RNA even when COVID-19 incidence rates were relatively low (~10 −5 ). We then used a computational downsampling approach to determine how using fewer replicate wells to measure the wastewater concentration reduced assay sensitivity and how the resultant reduction affected the ability to detect SARS-CoV-2 RNA at various COVID-19 incidence rates. When percent of positive droplets was between 0.024% and 0.5% (as was the case for SARS-CoV-2 genes during the Delta surge), measurements obtained with 3 or more wells were similar to those obtained using 10. When percent of positive droplets was less than 0.024% (as was the case prior to the Delta surge), then 6 or more wells were needed to obtain similar results as those obtained using 10 wells. When COVID-19 incidence rate is low (~ 10 −5 ), as it was before the Delta surge and SARS-CoV-2 gene concentrations are <10 4 cp/g, using 6 wells will yield a detectable concentration 90% of the time. Overall, results support an adaptive approach where assay sensitivity is increased by running 6 or more wells during periods of low SARS-CoV-2 gene concentrations, and 3 or more wells during periods of high SARS-CoV-2 gene concentrations. 

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