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
基本信息
- 批准号:2311297
- 负责人:
- 金额:$ 24.54万
- 依托单位:
- 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:
- 关键词:
项目摘要
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.
新冠肺炎夺走了近660万人的生命,让许多运行良好的医疗体系的繁荣国家变得更加虚弱。从这次大流行中吸取的一个重要教训是,在疫情爆发之初,非药物公共卫生干预对于抑制流行曲线至关重要。对正常生活影响最小的温和干预措施仍然能够有效地减少流行病的传播是非常可取的。例如,社会疏远和病例隔离等干预措施是遏制大流行的非常有效的战略。然而,在美国,此类缓解措施依赖于个人的自我报告机制,收集这些机制既耗时又容易出错。目前的项目旨在开发更准确和计算效率更高的统计工具,以在更广泛的战线上提高缓解措施的效率。这个项目为学生提供了多种独特的机会,让他们在统计学和生物监测的界面上参与前沿和跨学科的研究。在这个项目中,研究人员通过分析流动性数据,旨在开发一套分析和计算方法,使之能够及早发现疫情爆发和准确识别感染者。与自我报告机制相比,移动性数据包含非连续的个性化信息,并且可以很容易地从公共领域获得。联系人和移动性数据都可以自然地表示为网络(图),其中单个节点是位置或人(或一组人),其边(连接)对应于节点之间的接触或移动性的度量。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,使用大规模流动数据检测与传播路径和感染个体相关的潜在连接,以及对各种干预下网络之间的差异进行假设检验。该项目的结果将适用于广泛的生物监测任务,并将有助于我们整个社会的福祉。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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ping Ma其他文献
Assessment of Sediment Risk in the North End of Tai Lake, China: Integrating Chemical Analysis and Chronic Toxicity Testing with Chironomus dilutus
中国太湖北端沉积物风险评估:化学分析和摇蚊慢性毒性测试相结合
- DOI:
10.1007/s00244-015-0162-7 - 发表时间:
2015-05 - 期刊:
- 影响因子:4
- 作者:
Hongxue Qi;Ping Ma;Huizhen Li;Jing You - 通讯作者:
Jing You
Noninvasive imaging of hepatocyte IL-6/STAT3 signaling pathway for evaluating inflammation responses induced by end-stage stored whole blood transfusion
肝细胞IL-6/STAT3信号通路无创成像评估终末期储存全血输注引起的炎症反应
- DOI:
10.1007/s10529-019-02688-0 - 发表时间:
2019-05 - 期刊:
- 影响因子:2.7
- 作者:
Zhengjun Wang;Yulong Zhang;Qianqian Zhou;Ping Ma;Xiaohui Wang;Linsheng Zhan - 通讯作者:
Linsheng Zhan
Kindlin-2 Association with Rho GDP-Dissociation Inhibitor α Suppresses Rac1 Activation and Podocyte Injury
Kindlin-2 与 Rho GDP 解离抑制剂 α 的关联抑制 Rac1 激活和足细胞损伤
- DOI:
- 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Ying Sun;Chen Guo;Ping Ma;Yumei Lai;Fan Yang;Jun Cai;Yi Deng;Guozhi Xiao;Chuanyue Wu - 通讯作者:
Chuanyue Wu
Design of cold-formed thin-walled steel fixed-ended channels with complex edge stiffeners under axial compressive load by direct strength method
轴向压缩载荷下复杂边缘冷弯薄壁型钢固定端槽钢直接强度法设计
- DOI:
- 发表时间:
2012 - 期刊:
- 影响因子:0
- 作者:
Chun Gang Wang;Ping Ma;Dai Jun Song;Xin Yong Yu - 通讯作者:
Xin Yong Yu
Large-sized graphene oxide nanosheets increase DC–T cell synaptic contact and the efficacy of DC vaccines against SARS-CoV-2.
大尺寸氧化石墨烯纳米片可增加 DC-T 细胞突触接触以及 DC 疫苗针对 SARS-CoV-2 的功效。
- DOI:
10.1002/adma.202102528 - 发表时间:
2021 - 期刊:
- 影响因子:29.4
- 作者:
Qianqian Zhou;Hongjing Gu;Sujing Sun;Yulong Zhang;Yangyang Hou;Chenyan Li;Yan Zhao;Ping Ma;Liping Lv;Subi Aji;Shihui Sun;Xiaohui Wang;Linsheng Zhan - 通讯作者:
Linsheng Zhan
Ping Ma的其他文献
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{{ truncateString('Ping Ma', 18)}}的其他基金
ATD: Quantum algorithms for spatiotemporal models with applications to threat detection
ATD:时空模型的量子算法及其在威胁检测中的应用
- 批准号:
2319279 - 财政年份:2023
- 资助金额:
$ 24.54万 - 项目类别:
Standard Grant
ATD: Nonparametric Testing and Fast Computing Methods for Spatiotemporal Models with Applications to Threat Detection
ATD:时空模型的非参数测试和快速计算方法及其在威胁检测中的应用
- 批准号:
1925066 - 财政年份:2019
- 资助金额:
$ 24.54万 - 项目类别:
Standard Grant
Collaborative Research: ATD: Integrated statistical algorithms with ultra-high performance computing for discovering SNPs from massive next-generation metagenomic sequencing data
合作研究:ATD:将统计算法与超高性能计算相结合,用于从大量下一代宏基因组测序数据中发现 SNP
- 批准号:
1440037 - 财政年份:2013
- 资助金额:
$ 24.54万 - 项目类别:
Standard Grant
CAREER: Subsampling Methods in Statistical Modeling of Ultra-Large Sample Geophysics
职业:超大样本地球物理统计建模中的子采样方法
- 批准号:
1438957 - 财政年份:2013
- 资助金额:
$ 24.54万 - 项目类别:
Continuing Grant
Collaborative Research: ATD: Integrated statistical algorithms with ultra-high performance computing for discovering SNPs from massive next-generation metagenomic sequencing data
合作研究:ATD:将统计算法与超高性能计算相结合,用于从大量下一代宏基因组测序数据中发现 SNP
- 批准号:
1222718 - 财政年份:2012
- 资助金额:
$ 24.54万 - 项目类别:
Standard Grant
CAREER: Subsampling Methods in Statistical Modeling of Ultra-Large Sample Geophysics
职业:超大样本地球物理统计建模中的子采样方法
- 批准号:
1055815 - 财政年份:2011
- 资助金额:
$ 24.54万 - 项目类别:
Continuing Grant
Statistical Approaches to Integration of Mass Spectral and Genomic Data of Yeast Histone Modifications
酵母组蛋白修饰的质谱和基因组数据整合的统计方法
- 批准号:
0800631 - 财政年份:2008
- 资助金额:
$ 24.54万 - 项目类别:
Continuing Grant
CMG: Collaborative Research: Multi-Scale (Wave Equation) Tomographic Imaging with USArray Waveform Data
CMG:协作研究:使用 USArray 波形数据进行多尺度(波方程)断层成像
- 批准号:
0723759 - 财政年份:2007
- 资助金额:
$ 24.54万 - 项目类别:
Standard Grant
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