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Network based model of social media big data predicts contagious disease diffusion

Lauren S. Elkin (Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA)
Kamil Topal (Center for Proteomic and Bioinformatics, Department of Nutrition, Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA)
Gurkan Bebek (Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA)

Information Discovery and Delivery

ISSN: 2398-6247

Article publication date: 21 August 2017

671

Abstract

Purpose

Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks.

Design/methodology/approach

In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data.

Findings

The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets.

Originality/value

First, 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. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

Keywords

Acknowledgements

The authors would like to thank David Wise for thoughtful discussions and bringing this problem to our attention. This research was partially supported by a Grant from NIH/NCRR CTSA KL2TR000440 to GB.

Citation

Elkin, L.S., Topal, K. and Bebek, G. (2017), "Network based model of social media big data predicts contagious disease diffusion", Information Discovery and Delivery, Vol. 45 No. 3, pp. 110-120. https://doi.org/10.1108/IDD-05-2017-0046

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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