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 “代理和条件传输工具（TRAC）”程序将协同统计和 在三个应用领域的数学建模工作：1）结核病（TB）事件和传播； 2） 监测药物使用障碍（SUD）模式； 3）SARS COV-2传输建模。这三个 条件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艾滋病毒/艾滋病的高峰，而SARS COV-2引起了大流行 由于社会中断和死亡率超出了我们上个世纪所经历的一切。我们需要 改进的分析工具，利用现有数据监测这些疾病，推断传输热点， 确定干预措施的效率，并了解这些条件的燃烧。 该计划将汇集一个专家的定量研究人员，具有容易应用的技能 这些问题。我们还利用与临床研究人员和公共卫生官员的强大合作 确保我们开发的方法解决了重要问题，并与当前 了解这些疾病。通过创建一个计划来促进这些专家之间的沟通，我们 将在对这些疾病的关键方面进行建模并创造令人兴奋的方法论方面实现更大的创新 跨疾病的协同作用。我们的团队在整合新兴技术的数据方面都处于良好状态，包括 高通量测序数据以确定结核病风险特征并告知TB和SARS的传输链接 COV-2。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模式并确定风险特征。我们还将与我们的合作伙伴建立基础设施以进行分析 我们开发的工具为公共卫生从业人员和其他研究人员更容易获得。 这项工作的影响是开发一套分析工具，这些工具利用了迅速出现的丰富数据集为 提高我们对疾病传播模式的理解，监控这些状况的变化动态， 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 这些疾病并创建可再现的工具，可以持续使用。