Mental Health, Social Connectedness, and Fear During the COVID-19 Pandemic: A Qualitative Perspective from Older Women with HIV.

Older women with HIV (WWH) confront significant biopsychosocial challenges that may be exacerbated by the COVID-19 pandemic. Between May 2020 and April 2021, following a resiliency intervention conducted as part of a randomized parent trial, 24 cisgender WWH (M = 58 years old) completed quantitative assessments and qualitative interviews exploring the impact of COVID-19 on mental health. Qualitative data were analyzed via rapid analysis. Most participants were Black (62.5%) and non-Hispanic or Latina (87.5%). Emergent themes included (1) increased anxiety and depression; (2) a loss of social connectedness; (3) fear of unknown interactions among COVID-19, HIV, and other comorbidities; and (4) the use of largely adaptive strategies to cope with these issues. Findings suggest that older WWH face significant COVID-19-related mental health challenges, compounding existing stressors. As the pandemic persists, it will be important to assess the impact of these stressors on wellbeing, identify effective coping strategies, and provide increased support to mitigate COVID-19-related mental health issues over time. Trial Registration: ClinicalTrials.gov identifier: NCT03071887.

Suicide and Telehealth Treatments: A PRISMA Scoping Review.

BACKGROUND: This PRISMA scoping review explored worldwide research on the delivery of suicide-specific interventions through an exclusive telehealth modality. Research over telehealth modalities with suicidal individuals highlights the importance of facilitating participants' access to treatments despite location and circumstances (e.g., rural, expenses related to appointments, etc.). AIM: The review sought evidence of outcomes of trials or projects in which both the patient and therapist attended sessions conjointly and openly discussed suicide over a telehealth modality (e.g., phone, zoom). METHOD: To explore this topic the authors searched for research trials and quality improvement projects using Ovid Medline, Ovid Embase, Ovid PsycINFO, EBSCO Social Services Abstracts, and Web of Science on 3/3/2021. RESULTS: Nine different articles were included that each spanned distinct treatments, with eight being research studies and one being a quality improvement project. LIMITATIONS: Publications featuring ongoing or upcoming research in which complete study results were not available did not meet inclusion criteria for this review. CONCLUSION: Several important research gaps were identified. While this approach has been largely understudied, exclusive telehealth delivery of suicide-specific interventions has great potential for the prevention of suicidality, especially in the era of COVID-19 and beyond.

Cognitive concerns are a risk factor for mortality in people with HIV and coronavirus disease 2019.

BACKGROUND: Data supporting dementia as a risk factor for coronavirus disease 2019 (COVID-19) mortality relied on ICD-10 codes, yet nearly 40% of individuals with probable dementia lack a formal diagnosis. Dementia coding is not well established for people with HIV (PWH), and its reliance may affect risk assessment. METHODS: This retrospective cohort analysis of PWH with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR positivity includes comparisons to people without HIV (PWoH), matched by age, sex, race, and zipcode. Primary exposures were dementia diagnosis, by International Classification of Diseases (ICD)-10 codes, and cognitive concerns, defined as possible cognitive impairment up to 12 months before COVID-19 diagnosis after clinical review of notes from the electronic health record. Logistic regression models assessed the effect of dementia and cognitive concerns on odds of death [odds ratio (OR); 95% CI (95% confidence interval)]; models adjusted for VACS Index 2.0. RESULTS: Sixty-four PWH were identified out of 14 129 patients with SARS-CoV-2 infection and matched to 463 PWoH. Compared with PWoH, PWH had a higher prevalence of dementia (15.6% vs. 6%, P  = 0.01) and cognitive concerns (21.9% vs. 15.8%, P  = 0.04). Death was more frequent in PWH ( P  < 0.01). Adjusted for VACS Index 2.0, dementia [2.4 (1.0-5.8), P  = 0.05] and cognitive concerns [2.4 (1.1-5.3), P  = 0.03] were associated with increased odds of death. In PWH, the association between cognitive concern and death trended towards statistical significance [3.92 (0.81-20.19), P  = 0.09]; there was no association with dementia. CONCLUSION: Cognitive status assessments are important for care in COVID-19, especially among PWH. Larger studies should validate findings and determine long-term COVID-19 consequences in PWH with preexisting cognitive deficits.

Online Learning During the Covid Crisis: Student Views

In the spring of 2020, students at all levels of education were suddenly thrown into online learning situations. Higher education institutions reacted without regard or exposure to known best practices for online learning. As a result, student satisfaction levels dropped dramatically. Before Covid, those participating in online courses chose that option. Occasionally, an individual course would only be offered online, but for the majority, students who did not like online courses could avoid them. With the Covid crisis, all students were thrust into an online educational environment with no alternatives and little notice. To examine the learning during this crisis, we developed a 22-item scale on student perspectives of online learning and administered the survey to a large regional university in the southwest US during the Covid crisis (n=1160). We found online students prefer non-quantitative courses, are motivated by many scheduling issues, believe they learn less online and feel online students must be self-motivated and more disciplined, among other findings. © 2023, North American Business Press. All rights reserved.

Genomic Characterization of a Relative of Mumps Virus in Lesser Dawn Bats of Southeast Asia.

The importance of genomic surveillance on emerging diseases continues to be highlighted with the ongoing SARS-CoV-2 pandemic. Here, we present an analysis of a new bat-borne mumps virus (MuV) in a captive colony of lesser dawn bats (Eonycteris spelaea). This report describes an investigation of MuV-specific data originally collected as part of a longitudinal virome study of apparently healthy, captive lesser dawn bats in Southeast Asia (BioProject ID PRJNA561193) which was the first report of a MuV-like virus, named dawn bat paramyxovirus (DbPV), in bats outside of Africa. More in-depth analysis of these original RNA sequences in the current report reveals that the new DbPV genome shares only 86% amino acid identity with the RNA-dependent RNA polymerase of its closest relative, the African bat-borne mumps virus (AbMuV). While there is no obvious immediate cause for concern, it is important to continue investigating and monitoring bat-borne MuVs to determine the risk of human infection.

Manufacture and Characterization of Good Manufacturing Practice-Compliant SARS-COV-2 Cytotoxic T Lymphocytes.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 virus-specific cytotoxic T-cell lymphocytes (vCTLs) could provide a promising modality in COVID-19 treatment. We aimed to screen, manufacture, and characterize SARS-CoV-2-vCTLs generated from convalescent COVID-19 donors using the CliniMACS Cytokine Capture System (CCS). METHODS: Donor screening was done by stimulation of convalescent COVID-19 donor peripheral blood mononuclear cells with viral peptides and identification of interferonγ (IFN-γ)+ CD4 and CD8 T cells using flow cytometry. Clinical-grade SARS-CoV-2-vCTLs were manufactured using the CliniMACS CCS. The enriched SARS-CoV-2-vCTLs were characterized by T-cell receptor sequencing, mass cytometry, and transcriptome analysis. RESULTS: Of the convalescent donor blood samples, 93% passed the screening criteria for clinical manufacture. Three validation runs resulted in enriched T cells that were 79% (standard error of the mean 21%) IFN-γ+ T cells. SARS-CoV-2-vCTLs displayed a highly diverse T-cell receptor repertoire with enhancement of both memory CD8 and CD4 T cells, especially in CD8 TEM, CD4 TCM, and CD4 TEMRA cell subsets. SARS-CoV-2-vCTLs were polyfunctional with increased gene expression in T-cell function, interleukin, pathogen defense, and tumor necrosis factor superfamily pathways. CONCLUSIONS: Highly functional SARS-CoV-2-vCTLs can be rapidly generated by direct cytokine enrichment (12 hours) from convalescent donors. CLINICAL TRIALS REGISTRATION: NCT04896606.

TREM2+ and interstitial-like macrophages orchestrate airway inflammation in SARS-CoV-2 infection in rhesus macaques.

The immunopathological mechanisms driving the development of severe COVID-19 remain poorly defined. Here, we utilize a rhesus macaque model of acute SARS-CoV-2 infection to delineate perturbations in the innate immune system. SARS-CoV-2 initiates a rapid infiltration of plasmacytoid dendritic cells into the lower airway, commensurate with IFNA production, natural killer cell activation, and a significant increase of blood CD14-CD16+ monocytes. To dissect the contribution of lung myeloid subsets to airway inflammation, we generate a longitudinal scRNA-Seq dataset of airway cells, and map these subsets to corresponding populations in the human lung. SARS-CoV-2 infection elicits a rapid recruitment of two macrophage subsets: CD163+MRC1-, and TREM2+ populations that are the predominant source of inflammatory cytokines. Treatment with baricitinib (Olumiant®), a JAK1/2 inhibitor is effective in eliminating the influx of non-alveolar macrophages, with a reduction of inflammatory cytokines. This study delineates the major lung macrophage subsets driving airway inflammation during SARS-CoV-2 infection.

Beyond chart review: using NLP as a performance improvement tool to track aspirin utilization

Objective: As quality metrics increase, chart review to capture this data will not be feasible. Natural language processing (NLP) may offer solutions especially for lower resourced systems. ACOG and USPSTF recommend aspirin (ASA) to reduce the probability of developing preeclampsia. ASA is commonly only charted in free text in the electronic health record (EHR), rather than in the discrete field medication list. The aim of this study was to evaluate whether NLP could correctly identify patients for whom aspirin was indicated and who received it. Study Design: A retrospective cohort study of patients with clinic visits who delivered in two time periods: Oct 2019-Feb 2020 and Jan-Jun 2021. These were chosen to sample both pre- and post-COVID clinical documentation practices. To establish whether aspirin was received and indicated, chart review was performed for medications and risk factors/indications. We trained two Bidirectional Encoder Representations from Transformers (BERT)-based NLP models: first, a model to identify patients for whom aspirin had been given (Model 1: ASA Received);second, a model to identify patients for whom aspirin was indicated based on ACOG/USPSTF criteria (Model 2: ASA Indicated). Each model was then applied on the holdout data from the initial cohorts. Each model was also then applied on new, unseen data to evaluate its use for ongoing predictive performance (Mar-Jul 2022). Result(s): The models were trained on 824 patients, of whom 314 (38%) were positive per the ASA Indicated model and 92 (11%) were positive by the ASA Received model. Both models had excellent discriminatory abilities: ASA Received ROC AUC 0.87, ASA Indicated ROC AUC 0.91 (Figure 1). When applied to new data (Mar-Jul 2022), the models predicted frequencies of ASA indication and treatment which were stable and consistent with our performance trend (Figure 2). Conclusion(s): NLP represents a potential tool to monitor quality improvement processes without chart review. Since these models were developed on real-world EHR data, clinical dashboards could be created allowing more efficient and widespread system monitoring. [Formula presented] [Formula presented] Copyright © 2022

SARS-CoV-2 Outbreak Dynamics in an Isolated US Military Recruit Training Center With Rigorous Prevention Measures.

BACKGROUND: Marine recruits training at Parris Island experienced an unexpectedly high rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, despite preventive measures including a supervised, 2-week, pre-entry quarantine. We characterize SARS-CoV-2 transmission in this cohort. METHODS: Between May and November 2020, we monitored 2,469 unvaccinated, mostly male, Marine recruits prospectively during basic training. If participants tested negative for SARS-CoV-2 by quantitative polymerase chain reaction (qPCR) at the end of quarantine, they were transferred to the training site in segregated companies and underwent biweekly testing for 6 weeks. We assessed the effects of coronavirus disease 2019 (COVID-19) prevention measures on other respiratory infections with passive surveillance data, performed phylogenetic analysis, and modeled transmission dynamics and testing regimens. RESULTS: Preventive measures were associated with drastically lower rates of other respiratory illnesses. However, among the trainees, 1,107 (44.8%) tested SARS-CoV-2-positive, with either mild or no symptoms. Phylogenetic analysis of viral genomes from 580 participants revealed that all cases but one were linked to five independent introductions, each characterized by accumulation of mutations across and within companies, and similar viral isolates in individuals from the same company. Variation in company transmission rates (mean reproduction number R 0 ; 5.5 [95% confidence interval [CI], 5.0, 6.1]) could be accounted for by multiple initial cases within a company and superspreader events. Simulations indicate that frequent rapid-report testing with case isolation may minimize outbreaks. CONCLUSIONS: Transmission of wild-type SARS-CoV-2 among Marine recruits was approximately twice that seen in the community. Insights from SARS-CoV-2 outbreak dynamics and mutations spread in a remote, congregate setting may inform effective mitigation strategies.

Classification of neurologic outcomes from medical notes using natural language processing.

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach.

Side effects of COVID-19 or other vaccinations may affect an individual's safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00-10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.

Modulation of type I interferon responses potently inhibits SARS-CoV-2 replication and inflammation in rhesus macaques (preprint)

Type-I interferons (IFN-I) are critical mediators of innate control of viral infections, but also drive recruitment of inflammatory cells to sites of infection, a key feature of severe COVID-19. Here, and for the first time, IFN-I signaling was modulated in rhesus macaques (RMs) prior to and during acute SARS-CoV-2 infection using a mutated IFN2 (IFN-modulator; IFNmod), which has previously been shown to reduce the binding and signaling of endogenous IFN-I. In SARS-CoV-2-infected RMs, IFNmod reduced both antiviral and inflammatory ISGs. Notably, IFNmod treatment resulted in a potent reduction in (i) SARS-CoV-2 viral load in Bronchoalveolar lavage (BAL), upper airways, lung, and hilar lymph nodes; (ii) inflammatory cytokines, chemokines, and CD163+MRC1- inflammatory macrophages in BAL; and (iii) expression of Siglec-1, which enhances SARS-CoV-2 infection and predicts disease severity, on circulating monocytes. In the lung, IFNmod also reduced pathogenesis and attenuated pathways of inflammasome activation and stress response during acute SARS-CoV-2 infection. This study, using an intervention targeting both IFN- and IFN-{beta} pathways, shows that excessive inflammation driven by type 1 IFN critically contributes to SARS-CoV-2 pathogenesis in RMs, and demonstrates the potential of IFNmod to limit viral replication, SARS-CoV-2 induced inflammation, and COVID-19 severity.

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

BACKGROUND: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE: The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

SARS-CoV-2 Infections in Vaccinated and Unvaccinated Populations in Camp Lemonnier, Djibouti, from April 2020 to January 2022.

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the disparity between developed and developing countries for infectious disease surveillance and the sequencing of pathogen genomes. The majority of SARS-CoV-2 sequences published are from Europe, North America, and Asia. Between April 2020 and January 2022, 795 SARS-CoV-2-positive nares swabs from individuals in the U.S. Navy installation Camp Lemonnier, Djibouti, were collected, sequenced, and analyzed. In this study, we described the results of genomic sequencing and analysis for 589 samples, the first published viral sequences for Djibouti, including 196 cases of vaccine breakthrough infections. This study contributes to the knowledge base of circulating SARS-CoV-2 lineages in the under-sampled country of Djibouti, where only 716 total genome sequences are available at time of publication. Our analysis resulted in the detection of circulating variants of concern, mutations of interest in lineages in which those mutations are not common, and emerging spike mutations.

Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study.

BACKGROUND: Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. OBJECTIVE: We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. METHODS: We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. RESULTS: The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs -0.028). CONCLUSIONS: Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

Cancellations and delays of emergent orthopedic operations at a Canadian level 1 trauma centre.

BACKGROUND: Day-of surgery cancellation (DOSC) is considered to be a very inefficient use of hospital resources and results in emotional stress for the patient. To examine opportunities to minimize the incidence of preventable cancellations - an indicator of quality of care - we assessed the incidence of and reasons for DOSCs over 3 months among inpatients and outpatients at a trauma orthopedic service. METHODS: This was a prospective study of 2 cohorts of patients, inpatients and outpatients, scheduled for emergent orthopedic surgery at a Canadian tertiary level 1 trauma centre from Jan. 1 to Mar. 31, 2020. Patient demographic characteristics, injury characteristics, delays until surgery and reasons for DOSCs were recorded. RESULTS: A total of 185 patients (100 males and 85 females with a mean age of 54 yr) were included in the study. There were 98 outpatients and 87 inpatients. Seventy-five (40%) of the scheduled procedures in the outpatient group and 34 (30%) of those in the inpatient group were cancelled. In both groups, more than 85% of the cancellations were because of prioritization of a more urgent orthopedic or nonorthopedic surgical case. The average operative delay for the outpatient group was 11.4 days, compared to 3.8 days for the inpatient group (p < 0.001). CONCLUSION: High DOSC rates were observed among both outpatients and inpatients. The main reason for delaying surgery was prioritization of a more urgent surgical case. Providing the orthopedic trauma service with a dedicated OR opened 6 days per week, along with extended hours of OR services to 1700 daily, might be effective at minimizing DOSCs.

The Arabidopsis Framework Model version 2 predicts the organism-level effects of circadian clock gene mis-regulation (preprint)

Predicting a multicellular organism's phenotype quantitatively from its genotype is challenging, as genetic effects must propagate across scales. Circadian clocks are intracellular regulators that control temporal gene expression patterns and hence metabolism, physiology and behaviour. Here we explain and predict canonical phenotypes of circadian timing in a multicellular, model organism. We used diverse metabolic and physiological data to combine and extend mathematical models of rhythmic gene expression, photoperiod-dependent flowering, elongation growth and starch metabolism within a Framework Model for the vegetative growth of Arabidopsis thaliana, sharing the model and data files in a structured, public resource. The calibrated model predicted the effect of altered circadian timing upon each particular phenotype in clock-mutant plants under standard laboratory conditions. Altered night-time metabolism of stored starch accounted for most of the decrease in whole-plant biomass, as previously proposed. Mobilisation of a secondary store of malate and fumarate was also mis-regulated, accounting for any remaining biomass defect. We test three candidate mechanisms for the accumulation of these organic acids. Our results link genotype through specific processes to higher-level phenotypes, formalising our understanding of a subtle, pleiotropic syndrome at the whole-organism level, and validating the systems approach to understand complex traits starting from intracellular circuits. This work updates the first biorXiv version, February 2017, https://doi.org/10.1101/105437, with an expanded description and additional analysis of the same core data sets and the same FMv2 model, summary tables and supporting, follow-on data from three further studies with further collaborators. This biorXiv revision constitutes the second version of this report.

Genomic and Virological Characterization of SARS-CoV-2 Variants in a Subset of Unvaccinated and Vaccinated U.S. Military Personnel.

The emergence of SARS-CoV-2 variants complicates efforts to control the COVID-19 pandemic. Increasing genomic surveillance of SARS-CoV-2 is imperative for early detection of emerging variants, to trace the movement of variants, and to monitor effectiveness of countermeasures. Additionally, determining the amount of viable virus present in clinical samples is helpful to better understand the impact these variants have on viral shedding. In this study, we analyzed nasal swab samples collected between March 2020 and early November 2021 from a cohort of United States (U.S.) military personnel and healthcare system beneficiaries stationed worldwide as a part of the Defense Health Agency's (DHA) Global Emerging Infections Surveillance (GEIS) program. SARS-CoV-2 quantitative real time reverse-transcription PCR (qRT-PCR) positive samples were characterized by next-generation sequencing and a subset was analyzed for isolation and quantification of viable virus. Not surprisingly, we found that the Delta variant is the predominant strain circulating among U.S. military personnel beginning in July 2021 and primarily represents cases of vaccine breakthrough infections (VBIs). Among VBIs, we found a 50-fold increase in viable virus in nasal swab samples from Delta variant cases when compared to cases involving other variants. Notably, we found a 40-fold increase in viable virus in nasal swab samples from VBIs involving Delta as compared to unvaccinated personnel infected with other variants prior to the availability of approved vaccines. This study provides important insight about the genomic and virological characterization of SARS-CoV-2 isolates from a unique study population with a global presence.