QuBBD: Collaborative Research: Precision medicine and the management of infectious diseases

QuBBD：合作研究：精准医学和传染病管理

• 批准号: 1557742
• 负责人: Suchi Saria
• 金额: $ 1.58万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557742&HistoricalAwards=false
• 关键词: QuBBD; Collaborative; Research; Precision; medicine

Infectious diseases place an enormous toll on public health, societies, and economies across the world. Effective control of an infectious disease is made challenging by complex disease dynamics, limited resources, and the need to continually adapt interventions to the evolving status of an epidemic. Driven by widespread recognition of the potential of big data, recent technological advances have made it possible to collect, curate, and access heterogeneous data on the evolution of an infectious disease in real-time. In light of recent and anticipated advances in big data, this project envisions that future management of infectious diseases will rely on knowledge-transfer systems that map real-time, heterogeneous data streams to recommendations for policy-makers managing an infectious disease. These recommendations might identify subgroups in the population that should be given highest priority for interventions or other resource allocations. However, science is years away from creating such a system as its implementation will require significant innovations in disease modeling, data-driven decision making, computing, and optimization. This award supports initiation of a collaborative research project that takes critical first steps toward these innovations by creating a blueprint for using big data and precision medicine to inform management of an infectious disease.The proposed research will develop a novel class of dynamical systems models that identifies subgroups in the population with homogeneous disease dynamics. Parameters indexing these models are estimated using maximum likelihood; then, by computing draws from the sampling distribution of these parameters, we apply Thompson sampling and model-based policy-search algorithms from precision medicine to identify optimal resource allocations to manage the spread of an epidemic. The proposed research bridges dynamical systems models with subgroup identification and data-driven resource allocation. This will create new knowledge and new lines of research in applied mathematics, statistics, and computer science. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.

传染病给世界各地的公共卫生、社会和经济造成巨大损失。 由于复杂的疾病动态、有限的资源以及不断调整干预措施以适应流行病发展状况的需要，有效控制传染病面临着挑战。 在对大数据潜力的广泛认识的推动下，最近的技术进步使得实时收集、整理和访问有关传染病演变的异构数据成为可能。 鉴于大数据最近和预期的进展，该项目设想未来的传染病管理将依赖于知识转移系统，该系统将实时、异构数据流映射到为管理传染病的政策制定者提供建议。 这些建议可能会确定人口中应给予干预或其他资源分配最高优先级的亚群体。 然而，科学距离创建这样一个系统还需要数年时间，因为其实施需要在疾病建模、数据驱动决策、计算和优化方面进行重大创新。该奖项支持启动一个合作研究项目，该项目通过创建使用大数据和精准医学为传染病管理提供信息的蓝图，为这些创新迈出了关键的第一步。拟议的研究将开发一类新型动力系统模型，用于识别具有同质疾病动态的人群中的亚组。 使用最大似然估计索引这些模型的参数；然后，通过计算这些参数的抽样分布，我们应用汤普森抽样和精准医学中基于模型的策略搜索算法来确定最佳资源分配来管理流行病的传播。 所提出的研究将动态系统模型与子组识别和数据驱动的资源分配联系起来。 这将在应用数学、统计学和计算机科学领域创造新的知识和新的研究领域。 该奖项由美国国立卫生研究院大数据知识 (BD2K) 计划与美国国家科学基金会数学科学部合作支持。