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.

项目摘要 “物因和病症传播工具”方案将协同统计和 数学建模工作在三个领域的应用：1）结核病（TB）的发病率和传播; 2） 监测物质使用障碍（SUD）模式;以及3）SARS CoV-2传播建模。这三 结核病是主要的公共卫生问题，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超过了艾滋病毒/艾滋病的高峰期，SARS CoV-2导致了大流行 社会混乱和死亡率超过了我们在上个世纪所经历的任何事情。我们需要 改进分析工具，利用现有数据监测这些疾病，推断传播热点， 确定干预措施的有效性，并了解这些疾病的负担。 该计划将汇集一个定量研究人员的专家组，他们的技能很容易应用于 这些问题我们还利用与临床研究人员和公共卫生官员的密切合作 以确保我们开发的方法能够解决重要问题，并与我们目前的 了解这些疾病。通过创建一个程序来促进这些专家之间的沟通，我们 将在这些疾病的关键方面建模方面实现更大的创新， 疾病之间的协同作用。我们的团队能够很好地整合来自新兴技术的数据，包括 高通量测序数据，以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 二型冠状病毒我们在机器学习、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建立基础设施， 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，利用快速出现的丰富数据集， 提高我们对疾病传播模式的认识，监测这些情况的动态变化， 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，并创造可再生的工具，可以在一个持续的方式使用。