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: 2217239
• 负责人: Yanfang Ye
• 金额: $ 119.24万
• 依托单位: University of Notre Dame
• 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: 2021-11-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217239&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）大流行等传染病的爆发，伴随着时间和空间上的演变，导致了局部情况的发生，这对政策和决策者来说是一项艰巨的任务，需要在不同规模上寻找最佳的非药物干预（NPI）策略，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人工智能 (AI) 的先进能力，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，该框架适合不断变化的当地条件，并使专家能够适应性非营利机构在循环中有效应对流行病动态，同时平衡多维社会经济影响。拟议的工作不仅有助于有效应对公共卫生问题，同时减轻负面社会经济影响和各种引发的危机，使地方和联邦政府、地区社区、企业、社会领袖和公众受益，而且还将促进强有力的发展应对未来自然或人为灾害的基于科学的决策支持系统。该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、社会和行为科学。成果（例如开源代码、数据和模型）将通过出版物、媒体出版社等公开并广泛分发。该项目将研究与教育相结合，包括新颖的课程开发、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 外展活动。为了通过强有力的应对计划来应对传染病的爆发，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，允许在计算机上探索广泛的可能的 NPI在势场实施阶段之前。首先，该团队将开发一种新颖的时空异构图模型来抽象所利用的多源数据的动态。其次，该团队将开发新技术，通过集成空间和时间依赖性，同时保留异质性，来学习构建的图上的节点（即区域）表示。第三，基于学习到的节点表示，给定一组 NPI，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（即预测流行病动态和相关的社会经济影响）。第四，根据预测，该团队将开发一种具有逆向奖励学习的新型多智能体强化学习模型，使专家能够找到最佳的顺序 NPI，在一定的约束和目标设定下平衡流行病学效益和社会经济成本由政策和决策者。该研究将通过开发一系列原创作品，包括在异构和动态图结构背景下的新颖的深度图学习技术，推动信息集成和信息学领域的发展，这也将为解决未来自然或动态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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Rep2Vec: Repository Embedding via Heterogeneous Graph Adversarial Contrastive Learning

Rep2Vec：通过异构图对抗性对比学习进行存储库嵌入

• DOI: 10.1145/3534678.3539324
• 发表时间: 2022-08
• 期刊: ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD
• 作者: Qian, Yiyue;Zhang, Yiming;Wen, Qianlong;Ye, Yanfang;Zhang, Chuxu
• 通讯作者: Zhang, Chuxu

Few-Shot Learning on Graphs: A Survey

图上的少样本学习：一项调查

• 发表时间: 2022-01
• 期刊: The 31st International Joint Conference on Artificial Intelligence (IJCAI
• 作者: Zhang, Chuxu;Ding, Kaize;Li, Jundong;Zhang, Xiangliang;Ye, Yanfang;Chawla, Nitesh;Liu, Huan
• 通讯作者: Liu, Huan

Hyperbolic Graph Attention Network

双曲图注意力网络

• DOI: 10.1109/tbdata.2021.3081431
• 发表时间: 2021-05
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Zhang, Yiding;Wang, Xiao;Shi, Chuan;Jiang, Xunqiang;Ye, Yanfang
• 通讯作者: Ye, Yanfang

Differentially private binary- and matrix-valued data query: an XOR mechanism

差分私有二进制和矩阵值数据查询：XOR 机制

• DOI: 10.14778/3446095.3446106
• 发表时间: 2021-01
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Ji, Tianxi;Li, Pan;Yilmaz, Emre;Ayday, Erman;Ye, Yanfang;Sun, Jinyuan
• 通讯作者: Sun, Jinyuan

Heterogeneous Information Network Embedding with Adversarial Disentangler

具有对抗性解缠器的异构信息网络嵌入

• DOI: 10.1109/tkde.2021.3096231
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Wang, Ruijia;Shi, Chuan;Zhao, Tianyu;Wang, Xiao;Ye, Yanfang Fanny
• 通讯作者: Ye, Yanfang Fanny