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

尽管医疗技术和疫苗取得了进步，但SARS、甲型H1N1流感、禽流感、埃博拉和寨卡等新出现和重新出现的流行病继续构成巨大威胁。早期发现和立即反应对于避免社会影响至关重要。然而，在许多情况下，目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。 这些数据为开发更好的监测系统提供了机会。该项目将开发一个新的综合框架，用于根据实时地理标记的Twitter数据早期发现流行病爆发。这项工作结合了数学和计算机科学科学家的专业知识，将开发新的算法，以更快地检测（近实时和局部）流行病爆发。在局部地区有效地早期发现流行病将大大提高政府机构和卫生组织的认识，促进采取适当行动控制和治疗流行病。该项目将大大提高公众对流行病爆发的卫生意识和准备，并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发现流行病爆发。为此，该项目将在社区检测/聚类和地理标记的Twitter数据的热门话题分析中引入新的算法。这项工作还将定义有效的距离指标，联合收割机基础社交网络的结构，物理接近度和旅行信息来捕捉流行病传播的模式。 因此，偏微分方程模型的解决方案，描述了流行病传播的时空模式，提供了一个预警指标，预测即将爆发。此外，新的算法和偏微分方程的定理将揭示流行病的传播机制。该项目将产生一个新的跨学科的网络理论，数据挖掘和偏微分方程的流行病检测与地理标记的Twitter数据的框架。

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.