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.

传染病爆发，例如新型冠状病毒疾病（Covid-19）大流行，需要随时间和空间的进化而进行局部条件，这为政策和决策者提供了一项艰巨的任务，目的是在寻找最佳的非药物干预（NPI）策略，以不同的规模平衡流行性的效益和社会经济经济成本。通过利用数据革命并提高人工智能（AI）的能力来帮助解决这个具有挑战性的问题，这个多学科项目旨在设计和开发一个由数据驱动的和A-Aigment的框架，该框架量身定制，该框架量身定制，该框架是为了不断发展的本地化条件而定制的，并能够使专家的npis启用适应性的npis，以有效地响应跨性别的竞争性，而跨越了跨越跨越跨越跨越跨越跨越的竞争。拟议的工作不仅将使地方和联邦政府，区域社区，公司，社会领导人和公众受益，同时有效地应对公共卫生问题，同时减轻负面的社会经济影响和各种诱发危机，而且还将促进基于强大的科学支持系统对未来自然灾难做出反应的良好科学支持系统的发展。这项研究将对多学科领域有益，包括数据科学，机器学习，流行病学，经济学，社会和行为科学。 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一个智能和互动的决策支持框架，可以在潜在的现场实施阶段之前对大量可能的NPI进行计算机探索。首先，该团队将开发一种新型的时空异质图模型，以抽象利用多源数据的动态。其次，团队将通过在保留异质性的同时集成空间和时间依赖性来开发新技术来通过构造图来学习节点（即区域）表示。第三，基于学到的节点表示，鉴于一组NPI，该团队将设计和开发一种创新的NPI感知的多头变压器，以进行多任务预测（即预测流行性动力学和相关的社会经济影响）。第四，根据预测，团队将开发一种具有逆奖励学习的新型多项式增强学习模型，以使专家在寻找最佳的顺序NPI，以平衡在政策和决策者设定的某些限制和目标下，平衡流行病学利益和社会经济成本。这项研究将通过开发一系列原创作品，包括新颖的深图学习技术，并具有异构和动态的图形结构，这还将为解决未来的自然或人工灾难的类似挑战提供基本工作，以解决NSF的法定任务，并通过评估范围的范围，这是针对未来的自然或人工制造的奖项。