Maximizing Investigators' Research Award (R35)

项目总结 “传播病原体和病症的工具(TRAC)”计划将使统计和 三个应用领域的数学建模工作：1)结核病的发病率和传播；2) 监测物质使用障碍(SUD)模式；3)SARS CoV-2传播模型。这三个人 疾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， 在美国造成的死亡人数超过了艾滋病高峰期的死亡人数，以及导致大流行的SARS CoV-2 社会的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 改进的分析工具，利用现有数据监测这些疾病，推断传播热点， 确定干预措施的效果，并了解这些情况的负担。 这项计划将聚集一个由量化研究人员组成的专家组，他们的技能很容易应用于 这些问题。我们还利用我们与临床医生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解决了重要问题，并与我们当前的 对这些疾病的了解。通过创建一个计划来促进这些专家之间的交流，我们 将在对这些疾病的关键方面进行建模方面进行更大的创新，并创建令人兴奋的方法 跨疾病的协同效应。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告知结核病和SARS的传播链路 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大型数据库中的丰富信息，这些信息正在出现，以便更好地理解 SUD模式和识别风险特征。我们还将与我们的合作伙伴一起建设基础设施，以使分析 我们开发的工具更容易为公共卫生从业者和其他研究人员所用。 这项工作的影响是开发一套分析工具，利用快速涌现的丰富数据集来 提高我们对疾病传播模式的理解，监测这些情况的变化动态， 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 这些疾病并创造了可重复使用的工具，可持续使用。

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.