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 已夺去了近 660 万人的生命，并使许多医疗保健系统运行良好的繁荣国家变得更加脆弱。这次大流行的一个重要教训是，非药物公共卫生干预措施对于在疫情爆发之初抑制疫情曲线至关重要。对正常生活影响最小且仍能有效减少疫情传播的温和干预措施是非常可取的。例如，社交距离和病例隔离等干预措施是抑制疫情的非常有效的策略。然而，在美国，此类缓解措施依赖于个人的自我报告机制，收集起来耗时且容易出错。当前项目旨在开发更准确、计算效率更高的统计工具，以在更广泛的领域提高缓解措施的效率。该项目为学生提供了多种独特的机会，让他们参与统计和生物监测领域的前沿和跨学科研究。在该项目中，通过分析流动性数据，研究人员旨在开发一套分析和计算方法，使及早发现疫情爆发并准确识别感染者。与自我报告机制相比，移动数据包含非连续的个性化信息，并且可以轻松地从公共领域获取。接触和移动数据都可以自然地表示为网络（图），其中单个节点是一个位置或一个人（或一群人），其边（连接）对应于节点之间接触或移动的度量。该项目将开发一系列新颖的统计和机器学习方法，用于重建伪传播时间、识别感染个体、利用大规模流动性数据检测与传播途径和感染个体相关的潜在联系，并对差异进行假设检验在各种干预下的网络之间。该项目的成果将适用于广泛的生物监测任务，并将为我们整个社会的福祉做出贡献。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进行评估，认为值得支持以及更广泛的影响审查标准。