Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology

探险：合作研究：全球普适计算流行病学

• 批准号: 1918749
• 负责人: Aravind Srinivasan
• 金额: $ 40.64万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918749&HistoricalAwards=false
• 关键词: Expeditions; Collaborative; Research; Global; Pervasive

Infectious diseases cause more than 13 million deaths per year worldwide. Rapid growth in human population and its ability to adapt to a variety of environmental conditions has resulted in unprecedented levels of interaction between humans and other species. This rise in interaction combined with emerging trends in globalization, anti-microbial resistance, urbanization, climate change, and ecological pressures has increased the risk of a global pandemic. Computation and data sciences can capture the complexities underlying these disease determinants and revolutionize real-time epidemiology --- leading to fundamentally new ways to reduce the global burden of infectious diseases that has plagued humanity for thousands of years. This Expeditions project will enable novel implementations of global infectious disease computational epidemiology by advancing computational foundations, engineering principles, theoretical understanding, and novel technologies. The innovative tools developed will provide new analytical capabilities to decision makers and result in improved science-based decision making for epidemic planning and response. They will facilitate enhanced inter-agency and inter-government coordination and outbreak response. The team will work closely with many local, regional, national, and international public health agencies and universities to apply and deploy powerful technologies during epidemic outbreaks that can be expected to occur during the course of the project. International scientific networks linked to a comprehensive postdoctoral, graduate and undergraduate student training program will be established. Educational programs to foster interest in and increase understanding of computational science in addressing the complex societal challenges due to pandemics will also be developed. The team, with partners in Asia, Africa, Europe, and Latin America, will produce multidisciplinary scientists with diverse skills related to public health. The novel implementations of this project will be enabled by the development of a rigorous computational theory of spreading and control processes on dynamic multi-scale, multi-layer (MSML) networks, along with tools from AI, machine learning, and social sciences. New techniques resulting from this research will make it possible to develop and apply large-scale simulations of epidemics and social interactions over MSML networks. These simulations, in turn, will provide fundamentally new insights into how to control epidemics. Pervasive computing technologies will be developed to support disease surveillance and real-time response. The computational advances will also be generalizable; that is, they will be applicable to other areas such as cybersecurity, ecology, economics and social sciences. The project will take into account emerging concerns and constraints that include: preserving privacy of individuals and vulnerable groups, enabling model predictions to be interpreted and explained, developing effective interventions under uncertain and unknown network data, understanding strategic and adversarial behaviors of individual agents, and ensuring fairness of the process across the entire population. The research team includes experts from multiple disciplines and will address these societal concerns and constraints in practical, impactful, and novel ways, including the development of computational tools and techniques to support sound, ethical science-based policy pertaining to public health infectious disease epidemiology. Center for Computational Research in Epidemiology (CoRE) at the University of Virginia will be established as a part of the project. CoRE will develop transformative ways to support real-time epidemiology and facilitate improved outbreak response to benefit the society.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.

传染病每年导致全世界超过 1300 万人死亡。人口的快速增长及其适应各种环境条件的能力导致人类与其他物种之间的互动达到前所未有的程度。这种相互作用的增加，加上全球化、抗菌素耐药性、城市化、气候变化和生态压力等新兴趋势，增加了全球大流行的风险。计算和数据科学可以捕捉这些疾病决定因素背后的复杂性，并彻底改变实时流行病学——从而找到全新的方法来减轻困扰人类数千年的传染病的全球负担。该探险项目将通过推进计算基础、工程原理、理论理解和新技术，实现全球传染病计算流行病学的新颖实施。开发的创新工具将为决策者提供新的分析能力，并改善流行病规划和应对的科学决策。它们将促进加强机构间和政府间的协调和疫情应对。该团队将与许多地方、区域、国家和国际公共卫生机构和大学密切合作，在项目期间可能发生的流行病爆发期间应用和部署强大的技术。将建立与综合博士后、研究生和本科生培训计划相关的国际科学网络。还将制定教育计划，以培养人们对计算科学的兴趣并增进对计算科学的理解，以应对流行病造成的复杂社会挑战。该团队将与亚洲、非洲、欧洲和拉丁美洲的合作伙伴一起，培养具有公共卫生相关多种技能的多学科科学家。 该项目的新颖实施将通过动态多尺度、多层（MSML）网络上的传播和控制过程的严格计算理论的发展以及人工智能、机器学习和社会科学的工具来实现。这项研究产生的新技术将使开发和应用 MSML 网络上的流行病和社交互动的大规模模拟成为可能。这些模拟反过来将为如何控制流行病提供全新的见解。将开发普及计算技术来支持疾病监测和实时响应。 计算的进步也将是可推广的；也就是说，它们将适用于网络安全、生态学、经济学和社会科学等其他领域。该项目将考虑新出现的问题和限制，包括：保护个人和弱势群体的隐私，使模型预测能够得到解释和解释，在不确定和未知的网络数据下制定有效的干预措施，理解个体代理的战略和对抗行为，以及确保整个过程的公平性。该研究团队包括来自多个学科的专家，将以实用、有影响力和新颖的方式解决这些社会问题和限制，包括开发计算工具和技术，以支持与公共卫生传染病流行病学相关的合理、基于伦理科学的政策。作为该项目的一部分，将在弗吉尼亚大学建立流行病学计算研究中心（CoRE）。 CoRE 将开发变革性方法来支持实时流行病学并促进改善疫情应对，造福社会。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。

项目成果

Improved Bi-Point Rounding Algorithms and a Golden Barrier for k-median

改进的双点舍入算法和 k 中值的黄金屏障

• 发表时间: 2023
• 期刊: Proc. ACM-SIAM Symposium on Discrete Algorithms (SODA
• 作者: Gowda, K.;Pensyl, T.;Srinivasan, A.;Trinh, K.
• 通讯作者: Trinh, K.

Fair Clustering Under a Bounded Cost

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Seyed-Alireza Esmaeili;Brian Brubach;A. Srinivasan;John P. Dickerson

Barter Exchange with Shared Item Valuations

使用共享物品估价进行易货交换

• 发表时间: 2024
• 期刊: The Web Conference 2024 (formerly known as WWW
• 作者: Luque, Juan;Duppala, Sharmila;Dickerson, John;Srinivasan, Aravind
• 通讯作者: Srinivasan, Aravind

Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints

• DOI: 10.1609/aaai.v35i8.16842
• 发表时间: 2021-03
• 期刊: ArXiv
• 作者: Brian Brubach;D. Chakrabarti;John P. Dickerson;A. Srinivasan;Leonidas Tsepenekas

Differentially Private Partial Set Cover with Applications to Facility Location

• DOI: 10.48550/arxiv.2207.10240
• 发表时间: 2022-07
• 作者: George Z. Li;Dung Nguyen;A. Vullikanti