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

传染病的暴发，如新型冠状病毒病(新冠肺炎)大流行，导致局部情况随时间和空间的演变，这给政策制定者和决策者提出了一项艰巨的任务，即在不同的规模上寻找最佳的非药物干预战略，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提高人工智能(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AI增强的框架，该框架根据不断变化的本地化条件量身定做，并使适应性NPI的专家能够有效地应对流行病的动态，同时平衡多维的社会经济影响。拟议的工作不仅将帮助地方和联邦政府、区域社区、公司、社会领导人和公众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，而且还将促进建立强有力的以科学为基础的决策支持系统，以应对未来的自然灾害或人为灾害。这项研究将有益于多学科领域，包括数据科学、机器学习、流行病学、经济学、社会学和行为科学。结果(例如，开放源码、数据和模型)将通过出版物、媒体出版社等向公众公布并广泛传播。该项目将把研究与教育结合起来，包括新课程开发、学生指导、专业培训和劳动力发展，以及针对代表性不足群体的K-12外联活动。为了以强有力的应对规划抗击传染病暴发，该项目包括四个相互关联的研究组成部分，以开发一个智能和互动的决策支持框架，允许在潜在的现场实施阶段之前以电子方式探索广泛的可能的NPI。首先，该团队将开发一种新的时空异构图模型，以抽象利用的多源数据的动力学。其次，该团队将开发新的技术来学习构建的图上的节点(即区域)表示，方法是在保持异构性的同时整合空间和时间依赖关系。第三，基于学习的节点表示，在给定一组NPI的情况下，该团队将设计和开发一种创新的NPI感知多头变压器，用于多任务预测(即预测疫情动态和相关的社会经济影响)。第四，在预测的基础上，该团队将开发一种新的具有反向奖励学习的多智能体强化学习模型，以使环路专家能够在政策制定者和决策者设定的特定约束和目标下找到最优顺序NPI，以平衡流行病学收益和社会经济成本。这项研究将通过开发一系列原创作品来推动信息集成和信息学领域的发展，其中包括在异质和动态图形结构背景下的新型深度图形学习技术，这也将为应对未来自然或人为灾难的类似挑战提供基础工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.