Disease Propagation in Social Networks: A Novel Study of Infection Genesis and Spread on Twitter

author: Manan Shah, The Harker School
published: Oct. 12, 2016,   recorded: August 2016,   views: 1142

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


The CDC diagnoses millions of cases of infectious diseases annually with observed disease curves peaking around mid-December and lulling in August and September. While this provides an accurate depiction of disease spread, its compilation takes too long for up-to-date monitoring. The ability to generate real-time disease distributions is important in identifying outbreaks and facilitating instant communication between authorities and health-care providers. We have attempted to characterize disease propagation using Twitter, expanding upon Google’s 2008 Flu Trends project. Our novel contribution is the development of a pipeline based model incorporating natural language processing and machine learning. The correlation coefficient between the Twitter disease distribution obtained via our approach and CDC data was 0.98. Our model further identified disease outbreaks that were not prevalent in the CDC distribution such as the parotitis outbreak in late 2014 that large hospital samples failed to identify. We additionally develop a differential equation based disease simulation (known as SEIR) in order to further validate our Twitter disease distribution model. Our model has the potential to greatly assist in the creation of an early-warning infection system by identifying disease outbreaks in real-time using the ever-growing social media sphere, representing a unique and powerful benefit to society.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: