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

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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.