Abstract
Background: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam.
Method: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity.
Result: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models’ predictive capabilities.
Conclusion: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.
Keywords: COVID-19 severity; Clade; PRS; SARS-CoV-2; Vietnam.
【저자키워드】 SARS-CoV-2, COVID-19 severity, clade, PRS, Vietnam., 【초록키워드】 COVID-19, Treatment, coronavirus disease, Coronavirus disease 2019, immune response, Biomarker, Hospitalization, severity, SARS-CoV-2 variant, COVID-19 severity, risk, progression, delta variant, virus, clinical outcomes, global pandemic, COVID-19 treatment, Disease progression, Protein, Health, Predictive model, SARS-CoV-2 variants, severity of COVID-19, immune responses, Impact, Research, Random forest, clade, SNP, dataset, virus variants, SVM, therapeutic targets, COVID-19 patients, framework, COVID-19 patient, AUC, risk score, Healthcare system, therapeutic target, host cells, Predictive, host cell, Healthcare systems, Previous studies, comprehensive analysis, sequence, immunogenic, previous study, high accuracy, forest, country, prevalent, downstream analysis, affected, investigated, the disease, provided, Elastic, virus variant, 【제목키워드】 COVID-19, Sequencing, GWAS, investigation, host cell, immunogenic,