Maximizing Investigators' Research Award (R35)

最大化研究者研究奖（R35）

• 批准号: 10205596
• 负责人: Laura Forsberg White
• 金额: $ 17.19万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10205596
• 关键词: 2019-nCoV; AIDS/HIV problem; Address; Area; Award; Cessation of life; Collaborations; Communicable Diseases; Communication; Data; Data Set; Databases; Disease; Emerging Technologies; Ensure; High-Throughput Nucleotide Sequencing; Hot Spot; Incidence; Infrastructure; Intervention; Link; Machine Learning; Methodology; Methods; Modeling; Monitor; Pattern; Positioning Attribute; Public Health; Public Health Practice; Reproducibility; Research; Research Personnel; Risk; SARS-CoV-2 transmission; Statistical Models; Substance Use Disorder; Treatment Efficacy; Tuberculosis; United States; Work; analytical tool; disease transmission; experience; improved; innovation; mathematical model; mortality; pandemic disease; programs; skills; synergism; tool; transmission process

PROJECT SUMMARY The “Tools for Transmission of Agents and Conditions (TRAC)” program will synergize statistical and mathematical modeling work in three areas of application: 1) Tuberculosis (TB) incidence and transmission; 2) monitoring substance use disorder (SUD) patterns; and 3) SARS CoV-2 transmission modeling. These three conditions are major public health problems, with TB being the leading cause of infectious disease death globally, SUD causing more deaths in the United States than HIV/AIDS in its peak, and SARS CoV-2 causing a pandemic with societal disruption and mortality exceeding anything we have experienced in the last century. We need improved analytical tools that leverage existing data to monitor these diseases, infer transmission hot spots, determine the efficacy of interventions, and understand the burden of these conditions. This program will bring together an expert group of quantitative researchers with skills that are readily applied to these problems. We also leverage our strong collaborations with clinician researchers and public health officials to ensure that the methods we develop are addressing important questions and consistent with our current understanding of these diseases. By creating a program to facilitate communication between these experts, we will enable greater innovation in modeling key aspects of these diseases and create exciting methodological synergies across diseases. Our team is well positioned to incorporate data from emerging technologies, including high throughput sequencing data to determine TB risk signatures and inform transmission links for TB and SARS CoV-2. Our expertise in machine learning, a broad range of statistical methodologies, and mathematical modeling will enable us to leverage the rich information in large databases that are emerging to better understand SUD patterns and identify risk signatures. We will also build infrastructure with our partners to make the analytical tools that we develop more accessible to public health practitioners and other researchers. The impact of this work is to develop a suite of analytical tools that leverage rapidly emerging rich data sets to improve our understanding of disease transmission patterns, monitor changing dynamics of these conditions, and understand intervention strategies that are most effective. This work will inform public health practice for these diseases and create reproducible tools that can be used in an ongoing way.

项目概要 “媒介和条件传播工具（TRAC）”计划将协同统计和 数学建模工作在三个应用领域：1) 结核病 (TB) 发病率和传播；2) 监测物质使用障碍 (SUD) 模式；3) SARS CoV-2 传播模型。 结核病是全球传染病死亡的主要原因， SUD 在美国造成的死亡人数比高峰时期的艾滋病毒/艾滋病死亡人数还要多，而 SARS CoV-2 则造成了大流行 社会混乱和死亡率超过我们在上个世纪所经历的一切。 改进的分析工具，利用现有数据来监测这些疾病，推断传播热点， 确定干预措施的有效性，并了解这些情况的负担。 该计划将汇集一个由定量研究人员组成的专家组，这些研究人员具有易于应用的技能 我们还利用与临床研究人员和公共卫生官员的强有力合作。 确保我们开发的方法能够解决重要问题并符合我们当前的要求 通过创建一个计划来促进这些专家之间的交流，我们 将为这些疾病的关键方面建模带来更大的创新，并创造令人兴奋的方法论 我们的团队能够很好地整合新兴技术的数据，包括 高通量测序数据可确定结核病风险特征并为结核病和 SARS 的传播联系提供信息 我们在机器学习、广泛的统计方法和数学方面的专业知识。 建模将使我们能够利用正在出现的大型数据库中的丰富信息来更好地理解 SUD 模式并识别风险特征。我们还将与合作伙伴一起构建基础设施以进行分析。 我们开发的工具更容易被公共卫生从业者和其他研究人员使用。 这项工作的影响是开发一套分析工具，利用新兴的快速丰富的数据集 提高我们对疾病传播模式的理解，监测这些条件的动态变化， 并了解最有效的干预策略。这项工作将为公共卫生实践提供信息。 这些疾病并创建可以持续使用的可重复工具。