What is it about?
We predict future influeanza like illnesses (ILI) outbreaks and understand how they are spreading from location to location using social media data, namely, Twitter. We create a network model using neighborhood and airline traffic relationships among the states. Results show that flu-related data aligns well with ILI data from the Centers for Disease Control and Prevention (CDC) and the model can predict accurately the ILI activity.
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Why is it important?
Early detection Illnesses systems improve patient care providers. While extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA.
Perspectives
It is a pleasure to be a part of this interesting and helpful paper, which is a comprehensive ILI activity surveillance system. This article will lead more sophisticated ILI activity systems. We have already used social media data, geographic locations, airline traffic in the system. More can be added such as Natural Language Processing, epidemiological diffusion models, CDC data, and using more local geographic boundaries instead of states.
Gurkan Bebek
Case Western Reserve University
Read the Original
This page is a summary of: Network based model of social media big data predicts contagious disease diffusion, Information Discovery and Delivery, August 2017, Emerald,
DOI: 10.1108/idd-05-2017-0046.
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