Health Disparities and SARS-COV-2 Evolution: A Focused Viral Genomics Study

健康差异与SARS-CoV-2进化：一项有针对性的病毒基因组研究 项目摘要/摘要 我们假设，延长的COVID19疾病、再感染和/或疫苗后感染 慢性病患者与特定的SARS-CoV-2谱系或突变有关， 而且，提高对各种关切构成的潜在危险的认识将 改进测试遵从性和变体跟踪。我们建议使用我们建立的FDA- 授权的COVID19测序平台，结合创新的数据分析和 临床生物信息学以及我们的BMI和社区参与KCA。我们的建议 处理NOT-GM-21-031所指出的四个优先领域，具体如下： ·研究人群中是否存在不同的变种，病例数量是如何变化的 是由研究人群中不同的变种随时间变化引起的吗？ ·不同的变种在不同种族、民族、性别和/或年龄之间的分布情况 团体？ ·特定的变异是否与不同程度的COVID19症状的表现有关？ ·接种疫苗的研究参与者是否仍然感染SARS-CoV-2病毒，如果是，是什么变种 他们有货吗？ 我们提出以下具体目标： 具体目标1.通过整合SARS-CoV-2测序和 临床数据，重点是代表性不足、易受伤害和偏远人口。高使用率 自动化过程，我们将识别与临床表型相关的变异/突变， 包括长期无症状/抗体阳性、再次感染和接种疫苗的人 人口。所有趋势数据都将集成到AWS云中，通过该云可以访问 相关利益相关者，包括检测和疫苗项目合作伙伴。 具体目标2.开发和验证包含SARS-CoV-2基因的模拟模型 用临床结果预测路易斯安那州按地区划分的COVID19病例严重性的数据 疫苗接种水平提高。 具体目标3.设计和部署在文化和语言方面适当的宣传材料 并确定是否增加了对SARS-CoV-2适应潜在危险的了解 基因突变提高了检测和疫苗接种的接受度。

Health Disparities and SARS-CoV-2 Evolution: A Focused Viral Genomics Study Project Summary/Abstract We hypothesize that prolonged COVID19 illness, re-infection, and/or post-vaccine infection in patients with chronic conditions are associated with specific SARS-CoV-2 lineages or mutations, and that increasing the awareness of the potential danger posed by variants of concern will improve testing adherence and variant tracking. We propose to use our established FDA- authorized COVID19 sequencing platform in combination with innovative data analytics and clinical bioinformatics as well as our BMI and community engagement KCAs. Our proposal addresses four of the priority areas indicated by NOT-GM-21-031, specifically: • Are there different variants present in the study population, and how has the number of cases caused by different variants changed over time in the study population? • How are different variants distributed among different racial, ethnical, gender, and/or age groups? • Are specific variants associated with different levels of manifestation of COVID19 symptoms? • Do vaccinated study participants still acquire the SARS-CoV-2 virus, and if so, what variants do they carry? We propose these Specific Aims: Specific Aim 1. To improve surveillance of COVID19 by integrating SARS-CoV-2 sequencing and clinical data with a focus on under-represented, vulnerable and remote populations. Using highly automated processes, we will identify variants/mutations associated with clinical phenotypes, including prolonged asymptomatic/antibody-positive individuals, re-infected, and vaccinated populations. All trend data will be integrated in the AWS cloud where it can be accessed by relevant stakeholders including testing and vaccine program partners. Specific Aim 2. To develop and validate simulation models that incorporate SARS-CoV-2 genetic data with clinical outcomes to predict COVID19 case severity in Louisiana by region as vaccination levels increase. Specific Aim 3. To design and deploy culturally and linguistically appropriate outreach material on SARS-CoV-2 and determine whether increased knowledge of the potential danger of adaptive mutations improves acceptance of testing and vaccination.