III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks

III：媒介：数据驱动和人工智能增强的框架，用于对抗传染病爆发的协作决策

• 批准号: 2107172
• 负责人: Yanfang Ye
• 金额: $ 119.24万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107172&HistoricalAwards=false
• 关键词: III; Medium; Data; driven; AI

Infectious disease outbreaks, such as the novel coronavirus disease (COVID-19) pandemic, entailed localized conditions with evolution in time and space present a daunting task for policy and decision makers in finding optimal non-pharmaceutical intervention (NPI) strategies at different scales that balance epidemiological benefits and socioeconomic costs. To help tackle this challenging problem, by harnessing the data revolution and advancing capabilities of artificial intelligence (AI), this multidisciplinary project aims to design and develop a data-driven and AI-augmented framework that is tailored to the evolving localized conditions and enables expert-in-the-loop for adaptive NPIs to effectively respond to the dynamics of epidemic while balancing the multidimensional socioeconomic impacts. The proposed work will not only benefit local and federal governments, regional communities, corporations, societal leaders and the public by assisting with effective responses to the public health issues while mitigating negative socioeconomic impacts and various induced crises, but will also facilitate the development of robust science-based decision support systems responding to future natural or man-made disasters. The research will be beneficial to multidisciplinary areas, including data science, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes (e.g., open-source code, data, and models) will be made publicly accessible and broadly distributed through publications, media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities aimed at underrepresented groups.To combat infectious disease outbreaks with robust response planning, this project includes four interconnected research components to develop an intelligent and interactive decision support framework that allows in silico exploration of extensive possible NPIs prior to the potential field implementation phase. First, the team will develop a novel spatial-temporal heterogeneous graph model to abstract dynamics of harnessed multi-source data. Second, the team will develop new techniques to learn node (i.e., area) representations over the constructed graph by integrating both spatial and temporal dependencies while preserving the heterogeneity. Third, based on the learned node representations, given a set of NPIs, the team will design and develop an innovative NPI-aware multi-head transformer for multi-task prediction (i.e., forecasting epidemic dynamics and associated socioeconomic impacts). Fourth, based on the predictions, the team will develop a novel multi-agent reinforcement learning model with inverse reward learning to enable expert-in-the-loop in finding optimal sequential NPIs that balance epidemiological benefits and socioeconomic costs under certain constraints and objectives set by policy and decision makers. The research will advance the field of information integration and informatics through the development of a series of original works including novel deep graph learning techniques with the context of heterogeneous and dynamic graph structures, which will also provide foundational work for addressing similar challenges for future natural or man-made disasters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

新型冠状病毒病（COVID-19）等传染病爆发需要局部条件，并随着时间和空间的演变而演变，这对政策制定者和决策者来说是一项艰巨的任务，他们需要在不同规模上找到最佳的非药物干预（NPI）策略，以平衡流行病学效益和社会经济成本。为了帮助解决这一具有挑战性的问题，通过利用数据革命和推进人工智能（AI）的能力，这个多学科项目旨在设计和开发一个数据驱动和人工智能增强的框架，该框架针对不断变化的本地条件，并使专家在环自适应NPI能够有效地应对流行病的动态，同时平衡多维社会经济影响。拟议的工作不仅将有利于地方和联邦政府、区域社区、公司、社会领袖和公众，协助有效应对公共卫生问题，同时减轻负面的社会经济影响和各种诱发的危机，而且还将促进发展强大的基于科学的决策支持系统，以应对未来的自然或人为灾害。该研究将有利于多学科领域，包括数据科学，机器学习，流行病学，经济学，社会和行为科学。结果（例如，开放源代码、数据和模型）将通过出版物、媒体出版物等向公众开放并广泛传播。该项目将把研究与教育结合起来，包括新课程开发、学生辅导、专业培训和劳动力发展，以及针对代表性不足群体的K-12外展活动。为了通过强有力的应对规划来应对传染病爆发，该项目包括四个相互关联的研究部分，以开发一个智能和交互式决策支持框架，该框架允许在潜在的现场实施阶段之前对广泛的可能的NPI进行计算机探索。首先，该团队将开发一种新的时空异构图模型，以抽象所利用的多源数据的动态。其次，该团队将开发新的技术来学习节点（即，区域）表示，同时保持异质性。第三，基于学习到的节点表示，给定一组NPI，团队将设计和开发一种创新的NPI感知多头Transformer，用于多任务预测（即，预测流行病动态和相关的社会经济影响）。第四，基于预测，该团队将开发一种具有反向奖励学习的新型多智能体强化学习模型，以使专家能够找到最佳的连续NPI，在政策和决策者设定的某些限制和目标下平衡流行病学效益和社会经济成本。该研究将通过一系列原创作品的开发推进信息集成和信息学领域，包括在异构和动态图结构的背景下的新型深度图学习技术，这也将为解决未来自然或人类面临的类似挑战提供基础性工作-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。