ATD: An Integrated Framework of Network Theory, Data Mining and Partial Differential Equation for Early Detection of Epidemic Outbreaks

ATD：网络理论、数据挖掘和偏微分方程的集成框架，用于流行病爆发的早期检测

• 批准号: 1737861
• 负责人: Haiyan Wang
• 金额: $ 17.16万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737861&HistoricalAwards=false
• 关键词: ATD; Integrated; Framework; Network; Theory

Despite advancements in medical technology and vaccines, emerging and reemerging epidemics such as SARS, influenza A (H1N1), avian influenza, Ebola, and Zika continue to pose tremendous threats. Early detection and immediate response are essential to avoid societal repercussions. However, in many cases, current methods and algorithms for epidemic detection cannot account for the wealth of social media data available today. This data provides an opportunity to develop improved surveillance systems. This project will develop a novel integrated framework for early detection of epidemic outbreaks based on real-time geo-tagged Twitter data. The work combines the expertise of scientists in both mathematics and computer science and will develop new algorithms for faster detection (near real-time and localized) of epidemic outbreaks. Effective early detection of epidemics in localized regions will greatly increase governmental agency and health organization awareness, prompting appropriate actions to control and treat epidemics. This project will significantly enhance public health awareness and preparedness against epidemic outbreaks.The project will develop new methodologies and algorithms for early and accurate detection of epidemic outbreaks with social media data. To this end, the project will introduce new algorithms in community detection/clustering and hot topic analysis of geo-tagged Twitter data. The work will also define effective distance metrics that combine the structure of underlying social networks, physical proximity, and travel information to capture the pattern of epidemic spread. As a result, solutions of partial differential equation models, which describe spatio-temporal patterns of epidemic spread, are used to provide an early warning indicator for predicting imminence of an outbreak. In addition, new algorithms and theorems from partial differential equations will reveal epidemic spread mechanisms. The project will produce a new trans-disciplinary framework of network theory, data mining, and partial differential equations for epidemic detection with geo-tagged Twitter data.

尽管医疗技术和疫苗取得了进步，但SARS、甲型H1N1流感、禽流感、埃博拉病毒和寨卡病毒等新出现和重新出现的流行病仍然构成巨大威胁。早期发现和立即应对对于避免社会影响至关重要。然而，在许多情况下，当前的流行病检测方法和算法无法解释当今可用的社交媒体数据的丰富性。 这些数据为开发改进的监视系统提供了机会。该项目将开发一种新颖的集成框架，用于基于实时地理标记的 Twitter 数据及早发现流行病爆发。这项工作结合了数学和计算机科学领域科学家的专业知识，并将开发新的算法，以更快地检测（近实时和本地化）流行病爆发。有效及早发现局部地区的流行病将大大提高政府机构和卫生组织的认识，促使采取适当的行动来控制和治疗流行病。该项目将显着提高公共卫生意识和针对流行病爆发的准备。该项目将开发新的方法和算法，以便利用社交媒体数据及早准确地检测流行病爆发。为此，该项目将在社区检测/聚类和地理标记 Twitter 数据的热门话题分析方面引入新算法。这项工作还将定义有效的距离指标，结合底层社交网络的结构、物理接近度和旅行信息来捕捉流行病传播的模式。 因此，描述流行病传播时空模式的偏微分方程模型的解被用来提供预测爆发即将发生的预警指标。此外，来自偏微分方程的新算法和定理将揭示流行病传播机制。该项目将产生一个新的网络理论、数据挖掘和偏微分方程的跨学科框架，用于使用地理标记的 Twitter 数据进行流行病检测。

项目成果

Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona

使用偏微分方程和 Google Mobility 数据来预测亚利桑那州的 COVID-19

• DOI: 10.3934/mbe.2020266
• 发表时间: 2020-01
• 期刊: Mathematical Biosciences and Engineering
• 影响因子: 2.6
• 作者: Wang, Haiyan;Yamamoto, Nao
• 通讯作者: Yamamoto, Nao

Partial differential equation modeling of malware propagation in social networks with mixed delays

具有混合延迟的社交网络中恶意软件传播的偏微分方程建模

• DOI: 10.1016/j.camwa.2018.02.015
• 发表时间: 2018-05
• 期刊: Computers & Mathematics with Applications
• 影响因子: 2.9
• 作者: Du, Bo;Wang, Haiyan
• 通讯作者: Wang, Haiyan

Towards Understanding Community Interests With Topic Modeling

通过主题建模了解社区兴趣

• DOI: 10.1109/access.2018.2815904
• 发表时间: 2018-03-13
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Feng Wang;Kenneth Orton;Paul Wagenseller;Kuai Xu
• 通讯作者: Kuai Xu

Detecting fake news over online social media via domain reputations and content understanding

通过域名声誉和内容理解检测在线社交媒体上的假新闻

• DOI: 10.26599/tst.2018.9010139
• 发表时间: 2020-02-01
• 期刊: Tsinghua Science and Technology
• 影响因子: 6.6
• 作者: Kuai Xu;Feng Wang;Haiyan Wang;Bo Yang
• 通讯作者: Bo Yang

Regional Influenza Prediction with Sampling Twitter Data and PDE Model

利用 Twitter 采样数据和 PDE 模型预测区域流感

• DOI: 10.3390/ijerph17030678
• 发表时间: 2020-01-21
• 期刊: International Journal of Environmental Research and Public Health
• 作者: Yufang Wang;Kuai Xu;Yun Kang;Haiyan Wang;Feng Wang;Adrian Avram
• 通讯作者: Adrian Avram