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An examination of predictive disease saliency in social networks and the potential application of network analysis in locating disease spread

License: MIT License

Python 100.00%
network-analysis social-network disease-spread nlp

diseasenetworks's Introduction

Social Network Disease Analysis

Manan A. Shah 1 and Rok Sosic 2

1Student at the Harker School, San Jose, CA, USA
2The InfoLab, Stanford University School of Engineering, Stanford, CA, USA

Figure 1: An illustration of disease spread across a real airport network as a function of time.

Disease spread, both locally and across continents, has been studied in various medical contexts; however, the problem has yet to be thoroughly analyzed from an informatics standpoint. In this work, we plan to incorporate the vast array of big data presently available--specifically tweets on the social media site Twitter--in an attempt to model and predict disease genesis and dispersion over time. We further work towards generating cohesive equation-based models in the hope of comparing the results obtained from social media with both CDC ILI distributions and simulation curves.

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