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Introduction. In response to the COVID-19 pandemic in the Russian Federation, comprehensive response measures were taken. One of these measures was the development of a viral genome aggregation platform (VGARus) to monitor virus variability. The aim of this paper is to describe the role of the VGARus platform in tracking genetic variation in SARS-CoV-2. Materials and methods. VGARus utilizes sequencing data and bioinformatics tools to monitor genetic variations in SARS-CoV-2. The viral genomes were aligned using NextClade, which also translated them into amino acids and identified mutations. The viral variability over time was analyzed by counting the number of amino acid changes compared to the reference sequence. Results. The analysis of data within VGARus enabled the identification of new virus variants, contributing to improved diagnostic tests and vaccine development. The platform allowed for the prediction of epidemiologic trends, facilitating a rapid response to changes in the epidemiologic situation. For example, using VGARus, an increase in COVID-19 incidence was accurately predicted in the summer of 2022 and early 2023, which were associated with the emergence of Omicron subvariants BA.5 and XBB. Data from the platform helps validate the effectiveness of primers and DNA probes to ensure high diagnostic accuracy and reduce the risk of false negatives. Conclusion. VGARus demonstrates the growing role of genomic surveillance in combating COVID-19 and improving preparedness for future infectious disease outbreaks. The platform is a powerful tool for generating evidence-based solutions to combat a pandemic and mitigate its health, economic and societal impacts. It provides the ability to promptly obtain information on the epidemiologic situation in a particular region of the Russian Federation, use genomic data for phylogenetic analysis, compare the mutational spectrum of SARS-CoV-2 sequences with foreign samples. VGARus data allow for both retrospective analysis and predictive hypotheses. For example, we can clearly see the dynamics of the change of different virus variants: sequences belonging to the Alpha, Beta, Delta, Omicron lineages and many less common ones, clearly form the upsurges of morbidity, the interaction of which is reflected in the epidemiological picture. It is also currently being expanded to monitor other pathogens, increasing its public health relevance.
Introduction. In response to the COVID-19 pandemic in the Russian Federation, comprehensive response measures were taken. One of these measures was the development of a viral genome aggregation platform (VGARus) to monitor virus variability. The aim of this paper is to describe the role of the VGARus platform in tracking genetic variation in SARS-CoV-2. Materials and methods. VGARus utilizes sequencing data and bioinformatics tools to monitor genetic variations in SARS-CoV-2. The viral genomes were aligned using NextClade, which also translated them into amino acids and identified mutations. The viral variability over time was analyzed by counting the number of amino acid changes compared to the reference sequence. Results. The analysis of data within VGARus enabled the identification of new virus variants, contributing to improved diagnostic tests and vaccine development. The platform allowed for the prediction of epidemiologic trends, facilitating a rapid response to changes in the epidemiologic situation. For example, using VGARus, an increase in COVID-19 incidence was accurately predicted in the summer of 2022 and early 2023, which were associated with the emergence of Omicron subvariants BA.5 and XBB. Data from the platform helps validate the effectiveness of primers and DNA probes to ensure high diagnostic accuracy and reduce the risk of false negatives. Conclusion. VGARus demonstrates the growing role of genomic surveillance in combating COVID-19 and improving preparedness for future infectious disease outbreaks. The platform is a powerful tool for generating evidence-based solutions to combat a pandemic and mitigate its health, economic and societal impacts. It provides the ability to promptly obtain information on the epidemiologic situation in a particular region of the Russian Federation, use genomic data for phylogenetic analysis, compare the mutational spectrum of SARS-CoV-2 sequences with foreign samples. VGARus data allow for both retrospective analysis and predictive hypotheses. For example, we can clearly see the dynamics of the change of different virus variants: sequences belonging to the Alpha, Beta, Delta, Omicron lineages and many less common ones, clearly form the upsurges of morbidity, the interaction of which is reflected in the epidemiological picture. It is also currently being expanded to monitor other pathogens, increasing its public health relevance.
BACKGROUND At the end of December 2019, the world faced severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which led to the outbreak of coronavirus disease 2019 (COVID-19), associated with respiratory issues. This virus has shown significant challenges, especially for senior citizens, patients with other underlying illnesses, or those with a sedentary lifestyle. Serological tests conducted early on have helped identify how the virus is transmitted and how to curb its spread. The study hypothesis was that the rapid serological test for SARS-CoV-2 antibodies could indicate the immunoreactive profile during the COVID-19 pandemic in a university population. AIM To conduct active surveillance for serological expression of anti-SARS-CoV-2 antibodies in individuals within a university setting during the COVID-19 pandemic. METHODS This sectional study by convenience sampling was conducted in a large university in Niteroi-RJ, Brazil, from March 2021 to July 2021. The study population consisted of students, faculty, and administrative staff employed by the university. A total of 3433 faculty members, 60703 students, and 3812 administrative staff were invited to participate. Data were gathered through rapid serological tests to detect immunoglobulin (Ig) M and IgG against SARS-CoV-2. The χ ² or Fisher's exact test was used to conduct statistical analysis. A 0.20 significance level was adopted for variable selection in a multiple logistic regression model to evaluate associations. RESULTS A total of 1648 individuals were enrolled in the study. The proportion of COVID-19 positivity was 164/1648 (9.8%). The adjusted logistic model indicate a positive association between the expression of IgM or IgG and age [odds ratio (OR) = 1.16, 95%CI: 1.02-1.31] (P < 0.0024), individuals who had been in contact with a COVID-19-positive case (OR = 3.49, 95%CI: 2.34-5.37) (P < 0.001), those who had received the COVID-19 vaccine (OR = 2.33, 95%CI: 1.61-3.35) (P < 0.001) and social isolation (OR = 0.59, 95%CI: 0.41-0.84) (P < 0.004). The likelihood of showing a positive result increased by 16% with every ten-year increment. Conversely, adherence to social distancing measures decreased the likelihood by 41%. CONCLUSION These findings evidenced that the population became more exposed to the virus as individuals discontinued social distancing practices, thereby increasing the risk of infection for themselves.
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