Novel Analytical and Computational Approaches for Fusion and Analysis of Multi-Level and Multi-Scale Networks Data

用于多层次和多尺度网络数据融合和分析的新分析和计算方法

• 批准号: 2311297
• 负责人: Ping Ma
• 金额: $ 24.54万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311297&HistoricalAwards=false
• 关键词: Novel; Analytical; Computational; Approaches; Fusion

COVID-19 has claimed nearly 6.6 million lives and made many prosperous nations with well-run healthcare systems weaker. One important lesson learned from this pandemic is that non-pharmaceutical public health interventions are critical to suppress the epidemic curve at the beginning of the epidemic breakout. Mild interventions with minimal impact on normal life that are still capable to effectively reduce the epidemic spread are highly desirable. Such interventions as, for example, social distancing and case isolation are very effective strategies to suppress the pandemic. However, in the U.S., such mitigation measures rely on individuals' self-reporting mechanisms, which are time-consuming to collect and error-prone. The current project aims to develop more accurate and computationally efficient statistical tools to enhance efficiency of mitigation measures at a broader front. This project offers multiple unique opportunities for students to participate in cutting-edge and interdisciplinary research at the interface of statistics and bio-surveillance.In this project, by analyzing mobility data, the investigators aim to develop a suite of analytical and computational approaches that enables the early detection of the epidemic outbreak and accurate identification of infected individuals. Compared to self-reporting mechanisms, mobility data contains non-continuous individualized information and can be easily obtained from the public domain. Both the contact and mobility data can be naturally represented as networks (graphs), where the individual node is a location or a person (or a group of people), and its edges (connections) correspond to measures of contact or mobility between the nodes. The project will develop a series of novel statistical and machine learning methods for reconstructing pseudo-transmission time, identifying the infected individuals, detecting potential connections related to transmission pathways and infectious individuals using large-scale mobility data, as well as hypothesis testing for the differences between networks under various interventions. The results of the project will be applicable to a wide range of bio-surveillance tasks and will contribute to the wellbeing of our society as a whole.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.

Covid-19夺去了近660万人的生命，并使许多繁荣国家的医疗保健系统较弱。从这个大流行中学到的一个重要的教训是，非药物公共卫生干预措施对于在流行病突破开始时抑制流行曲线至关重要。非常需要有效地减少流行病的正常生命的轻度干预措施对正常生活的影响最小。例如，例如，社会疏远和隔离案例是抑制大流行的非常有效的策略。但是，在美国，这种缓解措施依赖于个人的自我报告机制，这些机制耗时，这些机制耗时易于收集和容易出错。当前的项目旨在开发更准确和计算上有效的统计工具，以提高更广泛的方面缓解措施的效率。该项目为学生提供了多个独特的机会，可以在统计和生物保育的界面上参与尖端和跨学科研究。在该项目中，通过分析流动性数据，研究人员旨在开发一系列分析和计算方法，从而能够早期发现流行病爆发和被感染个体的准确发现。与自我报告的机制相比，移动性数据包含非连续的个性化信息，并且可以轻松从公共领域获得。触点和移动性数据都可以自然表示为网络（图），其中单个节点是位置或一个人（或一组人），其边缘（连接）对应于节点之间的接触或移动性度量。该项目将开发一系列新颖的统计和机器学习方法，用于重建伪传输时间，识别受感染的个体，检测使用大规模流动性数据以及在各种介入下网络之间差异的假设测试的潜在连接和传染性个人有关。该项目的结果将适用于广泛的生物保育任务，并将为整个社会的福祉做出贡献。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点评估来支持，并具有更广泛的影响。