Mathematical and Computational Predictive Modeling Core

数学和计算预测建模核心

• 批准号: 10256815
• 负责人: Jason HT Bates
• 金额: $ 16.2万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256815
• 关键词: Address; Affect; Algorithms; Behavioral; Biomedical Computing; Brain; Centers of Research Excellence; Clinical; Collaborations; Communicable Diseases; Complex; Computer Models; Consultations; Data; Data Set; Development; Disease; Drug resistance; Education; Faculty; Goals; Human; Immunity; Immunologics; Infectious Diseases Research; Inflammatory; Intervention; Intuition; Knowledge; Laboratories; Mathematics; Measures; Predisposition; Preparation; Process; Public Health; Resources; Scientist; Services; Testing; Therapeutic; Training; Translating; Translational Research; Trust; Vaccines; biomedical scientist; computerized tools; design; disease natural history; disease transmission; exhaustion; experience; innovation; knowledge base; large datasets; meetings; microbiome; model building; next generation; outcome prediction; pathogen; predictive modeling; predictive test; pressure; prevent; programs; tool; transmission process

PROJECT SUMMARY – Mathematical and Computational Predictive (MCP) Modeling Core Reducing the burden of global infectious disease presents a number of stubborn key challenges. Principle among these are 1) to understand the dynamic interface (immunologic, inflammatory, microbiome-related, pathophysiologic) between an infectious pathogen and its human host, particularly in terms of the complex factors that determine host susceptibility to or protection from clinical disease, 2) to understand the dynamics of disease transmission and how they are affected by environmental and behavioral pressures, and 3) to predict and measure the impact of interventions (vaccine, therapeutic, public health) with the goal of working toward global improvements in public health. Meeting these challenges requires not only a broad knowledge base that includes the natural history of the disease, its intra-host mechanism of action, its inter-host mode of transmission, and large sets of complex -omic and demographic data, but also the ability to integrate this knowledge into quantitative predictions of outcomes. This can only be done effectively when the relevant processes are translated into mathematical expressions or algorithms that are implemented computationally so that their predictions can be exhaustively examined. The goal of the Mathematical and Computational Predictive (MCP) Modeling Core is to provide the expertise and resources necessary to bring MCP modeling to the COBRE, with special focus on the junior faculty projects. MCP services include: 1) design, innovation and planning, in consultation with the “Brains Trust,” 2) curation and preparation of final analytic datasets, and 3) predictive model building and testing. By direct interaction with the COBRE faculty, its educational components, and use of the “Innovation and Collaboration” laboratory, the MCP Modeling core will bridge the scientific “culture gap” between the scientists with biomedical backgrounds and those with computational modeling expertise. This will greatly enhance the ability of the TGIR Center to advance the understanding and management of global infectious diseases.

项目总结-数学和计算预测(MCP)建模核心 减轻全球传染病的负担带来了一些顽固的关键挑战。原理 其中包括1)了解动态界面(免疫学、炎症性、微生物组相关， 病理生理学)在感染病原体和它的人类宿主之间，特别是在复合体方面 决定宿主对临床疾病的易感性或对临床疾病的保护的因素，2)了解动态 疾病传播及其如何受到环境和行为压力的影响，以及3) 预测和衡量干预措施(疫苗、治疗、公共卫生)的影响，以实现工作目标 致力于全球公共卫生的改善。迎接这些挑战不仅需要广博的知识 包括该病的自然病史、其在宿主内的作用机制、其宿主间模式 传输，以及大量复杂的组学和人口统计数据，而且还有整合这些数据的能力 将知识转化为对结果的量化预测。这只有在相关的情况下才能有效地完成 过程被转换为通过计算实现的数学表达式或算法 他们的预测可以被彻底检验。数学与计算的目标 预测(MCP)建模的核心是提供必要的专业知识和资源，使MCP建模 科布雷，特别关注初级教员项目。MCP服务包括：1)设计、创新和 规划，与“智囊团”协商，2)最终分析数据集的策划和准备，3) 预测模型的建立和测试。通过与科布雷教职员工的直接互动，其教育 组件，并使用“创新与协作”实验室，MCP建模核心将架起 有生物医学背景的科学家和有计算背景的科学家之间的科学“文化鸿沟” 模特方面的专业知识。这将极大地提高TGIR中心的能力，促进对 全球传染病的管理。