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Almost all published rooting and dating studies on SARS-CoV-2 assumed that (1) evolutionary rate does not change over time although different lineages can have different evolutionary rates (uncorrelated relaxed clock), and (2) a zoonotic transmission occurred in Wuhan and the culprit was immediately captured, so that only the SARS-CoV-2 genomes obtained in 2019 and the first few months of 2020 (resulting from the first wave of the global expansion from Wuhan) are sufficient for dating the common ancestor. Empirical data contradict the first assumption. The second assumption is not warranted because mounting evidence suggests the presence of early SARS-CoV-2 lineages cocirculating with the Wuhan strains. Large trees with SARS-CoV-2 genomes beyond the first few months are needed to increase the likelihood of finding SARS-CoV-2 lineages that might have originated at the same time as (or even before) those early Wuhan strains. I extended a previously published rapid rooting method to model evolutionary rate as a linear function instead of a constant. This substantially improves the dating of the common ancestor of sampled SARS-CoV-2 genomes. Based on two large trees with 83,688 and 970,777 high-quality and full-length SARS-CoV-2 genomes that contain complete sample collection dates, the common ancestor was dated to 12 June 2019 and 7 July 2019 with the two trees, respectively. The two data sets would give dramatically different or even absurd estimates if the rate was treated as a constant. The large trees were also crucial for overcoming the high rate-heterogeneity among different viral lineages. The improved method was implemented in the software TRAD.

Research documenting the impact of COVID-19 on Two-Spirit, lesbian, gay, bisexual, transgender, and queer (2SLGBTQ+) populations in Canada is limited. Our objectives were to investigate the impact of COVID-19 lockdown measures on the lives of trans, nonbinary, and other gender nonconforming (TGNC) people. Engage COVID-19 is a mixed methods study examining the impact of COVID-19 on gay, bisexual, queer, and other men who have sex with men (GBQM) living in Vancouver, Toronto, and Montreal, Canada. Using purposive sampling, we conducted in-depth qualitative interviews (between November 2020–February 2021 and June–October 2021) with 93 participants who discussed the impact of COVID-19 on their lives. Seventeen participants were identified as TGNC. TGNC participants reported barriers to trans healthcare during the initial months of the COVID-19 pandemic. Several participants indicated that some public health interventions during COVID-19 (i.e., lockdowns) eased the pressure to “perform” gender due to fewer in-person interactions. During lockdowns, TGNC participants increasingly cultivated community networks online. Nevertheless, participants reported longing for the social support that was available to them during pre-COVID. Lack of access to community spaces during lockdowns had a negative impact on participants’ mental health, despite reduced pressure to perform gender and opportunities for social engagement in online spaces.

Background: To mitigate the adverse effects of the COVID-19 pandemic on financial resources, governments and family/friends mobilized financial support interventions (e.g., emergency aid funds) and assistance. However, little is known about how financial assistance alleviated mental health problems. This study aimed to investigate the moderating effect of financial support from the government or from family/friends on the association between income loss and depression among young adults. Methods: Two online cross-sectional surveys among young adults ages 18–29 living in Canada and France were conducted in 2020 (n = 4,511) and 2021 (n = 3,329). Moderate-to-severe depressive symptoms were measured using the Patient Health Questionnaire-9 (cut-off score: ≥10). Two logistic regression models were performed for each survey with an interaction term between income loss and financial support (government or family/friends modeled separately), controlling for demographics. Results: Overall, half reported depressive symptoms (2020/2021: 53.5%/45.6%), and over a third lost income (2020/2021: 10.2%/11.6% all income, 37.7%/21.6% some income). In 2020, 40.6% received government financial support (17.7% in 2021) while family/friends support was received by 12% (in both surveys). In both surveys, among those who received governmental financial support, income loss was associated with depression, whether participants lost all their income (e.g., 2020: Adjusted Odds Ratios (AOR) 1.75, 95% Confidence Interval [1.29–2.44]), or some of their income (e.g., 2020: AOR 1.45 [1.17–1.81]). However, among those who received family/friends financial support, income loss was no longer significantly associated with depression in both cycles, whether participants lost all their income (e.g., 2020: AOR 1.37 [0.78–2.40]), or some of their income (e. g., 2020: AOR 1.31 [0.86–1.99]).

Introduction: There is an increasing demand on hospital capacity worldwide due to the COVID-19 pandemic and local staff shortages. Novel care pathways have to be developed in order to keep bariatric and metabolic surgery maintainable. Same-day discharge (SDD) after laparoscopic Roux-en-Y gastric bypass (RYGB) is proved to be feasible and could potentially solve this challenge. The aim of this study was to investigate whether SDD after RYGB is safe for a selected group of patients. Methods: In this single-center cohort study, low-risk patients were selected for primary RYGB with intended same-day discharge with remote monitoring. All patients were operated according to ERAS protocol. There were strict criteria on approval upon same-day discharge. It was demanded that patients should contact the hospital in case of any signs of complications. Primary outcome was the rate of successful same-day discharge without readmission within 48?h. Secondary outcomes included short-term complications, emergency department visits, readmissions, and mortality. Results: Five hundred patients underwent RYGB with intended SDD, of whom 465 (93.0%) were successfully discharged. Twenty-one patients (4.5%) were readmitted in the first 48?h postoperatively. None of these patients had a severe bleeding. This results in a success rate of 88.8% of SDD without readmission within 48?h. Conclusions: Same-day discharge after RYGB is safe, provided that patients are carefully selected and strict discharge criteria are used. It is an effective care pathway to reduce the burden on hospital capacity. Graphical Abstract: [Figure not available: see fulltext.]

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

As the Coronavirus Disease 2019 (COVID-19) continues to impact many aspects of life and the global healthcare systems, the adoption of rapid and effective screening methods to prevent the further spread of the virus and lessen the burden on healthcare providers is a necessity. As a cheap and widely accessible medical image modality, point-of-care ultrasound (POCUS) imaging allows radiologists to identify symptoms and assess severity through visual inspection of the chest ultrasound images. Combined with the recent advancements in computer science, applications of deep learning techniques in medical image analysis have shown promising results, demonstrating that artificial intelligence-based solutions can accelerate the diagnosis of COVID-19 and lower the burden on healthcare professionals. However, the lack of large, well annotated datasets poses a challenge in developing effective deep neural networks, especially in the case of rare diseases and new pandemics. To address this issue, we present COVID-Net USPro, an explainable few-shot deep prototypical network that is designed to detect COVID-19 cases from very few ultrasound images. Through intensive quantitative and qualitative assessments, the network not only demonstrates high performance in identifying COVID-19 positive cases, using an explainability component, but it is also shown that the network makes decisions based on the actual representative patterns of the disease. Specifically, COVID-Net USPro achieves 99.55% overall accuracy, 99.93% recall, and 99.83% precision for COVID-19-positive cases when trained with only five shots. In addition to the quantitative performance assessment, our contributing clinician with extensive experience in POCUS interpretation verified the analytic pipeline and results, ensuring that the network’s decisions are based on clinically relevant image patterns integral to COVID-19 diagnosis. We believe that network explainability and clinical validation are integral components for the successful adoption of deep learning in the medical field. As part of the COVID-Net initiative, and to promote reproducibility and foster further innovation, the network is open-sourced and available to the public.

This paper uses “prolepsis,” a process of reaching into the past to inform present and future practices, to understand 12 English-as-a-second language (ESL) teachers’ practices of supporting English language learners (ELLs) through remote teaching during the COVID-19 pandemic from 2020-2021 in British Columbia and to envision some different current and future post-pandemic classroom literacies for diverse learners. Accounts of these ESL teachers’ synthetical moments of teaching and supporting ELLs during the pandemic suggest that they had to navigate “new” areas of teaching, including attending to students’ social-emotional learning (SEL), connecting with ELL parents, teaching and engaging students via technology-supported instruction, and co-teaching with mainstream teachers, on the basis of limited or no pre-pandemic experience. These insights suggest a need to widen the focus on ESL teachers’ knowledge and expertise in applied linguistics and instructional strategies to include classroom literacies in integrating SEL into ESL instruction, adopting interactive, student-driven instructional designs and practices afforded by multimodal technologies, maintaining multiple channels of communication with parents and students, and team-teaching with classroom teachers to provide tailored language support for ELLs.

Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method trains a nine-state Hidden Markov Model (HMM) initialized from a biclustering of 23 features extracted from online messages. We apply our method to 16,000 conversations (1.5 million messages) that took place on the Facebook pages of 15 Canadian newspapers following COVID-19 news items, and show that it can effectively extract the conversation structure and discover the main themes of the messages. Furthermore, we demonstrate how the PageRank algorithm and the conversation graph discovered can be used to simulate the impact of five different moderation strategies, which makes it possible to easily develop and test new strategies to limit the spread of harmful messages. Although our work in this paper focuses on the COVID-19 pandemic, the methodology is general enough to be applied to handle communications during future pandemics and other crises, or to develop better practices for online community moderation in general.

L’adaptation psychologique et sociale et la réussite dans les établissements postsecondaires sont soutenues par un sentiment d’appartenance à un groupe social et par des relations significatives avec les autres étudiants, le personnel et les membres du corps professoral. Cette étude exploratoire utilise une approche qualitative pour enquêter sur le sentiment d’appartenance à l’environne-ment d’apprentissage virtuel des étudiants de niveau postsecondaire pendant la pandémie de COVID-19. L’étude a été menée dans une petite université de l’Ouest canadien. Une entrevue semi-dirigée a été menée auprès de vingt étudiants de premier cycle, de diverses facultés et années d’études. Les résultats ont été regroupés sous trois thématiques : (1) les attentes des étudiants vis-à-vis de l’université; (2) l’impact des environnements d’apprentissage virtuels sur les étudiants; et (3) le rôle des enseignants. Des recommandations sont proposées afin d’améliorer le soutien et l’appartenance des étudiants de niveau postsecondaire dans les environnements d’apprentissage virtuels. Psychological and social adjustment and academic success in post-secondary institutions are supported by a sense of belonging to a social group and having meaningful relationships with other students, staff, and faculty members. This exploratory study used a qualitative approach to investigate post-secondary students’ sense of belonging in the virtual learning environment during the COVID-19 pandemic. The study was conducted at a small Western Canadian university. Semi-structured interviews were conducted with 20 participants who were undergraduate students, from various faculties, and in different years in their programs. Findings were clustered into three themes: (1) student expectations of university, (2) impact of virtual learning environments on students, and (3) the role of educators. Recommendations are included to enhance support and belonging for post-secondary students in virtual learning environments.

Individuals infected with the SARS-CoV-2 virus present with a wide variety of symptoms ranging from asymptomatic to severe and even lethal outcomes. Past research has revealed a genetic haplotype on chromosome 3 that entered the human population via introgression from Neanderthals as the strongest genetic risk factor for the severe response to COVID-19. However, the specific variants along this introgressed haplotype that contribute to this risk and the biological mechanisms that are involved remain unclear. Here, we assess the variants present on the risk haplotype for their likelihood of driving the genetic predisposition to severe COVID-19 outcomes. We do this by first exploring their impact on the regulation of genes involved in COVID-19 infection using a variety of population genetics and functional genomics tools. We then perform a locus-specific massively parallel reporter assay to individually assess the regulatory potential of each allele on the haplotype in a multipotent immune-related cell line. We ultimately reduce the set of over 600 linked genetic variants to identify four introgressed alleles that are strong functional candidates for driving the association between this locus and severe COVID-19. Using reporter assays in the presence/absence of SARS-CoV-2, we find evidence that these variants respond to viral infection. These variants likely drive the locus’ impact on severity by modulating the regulation of two critical chemokine receptor genes: CCR1 and CCR5. These alleles are ideal targets for future functional investigations into the interaction between host genomics and COVID-19 outcomes.