An investigation into social media syndromic monitoring. Issue 8 (14th September 2017)

• Record Type: Journal Article
• Authors: Sparks, Ross S.; Robinson, Bella; Power, Robert; Cameron, Mark; Woolford, Sam
• Is Part Of: Communications in statistics. Volume 46:Issue 8(2017)
• Page Start: 5901
• Page End: 5923
• Publication Date: 2017-09-14
• Subjects: Dynamic bi-plot -- Monitoring -- Multivariate syndrome surveillance -- Outbreak detection -- Text mining; 62L10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1186182
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Tweets offer us early information on initial stages of diseases, since people often tweet the early symptoms of feeling unwell prior to presenting to an emergency department if their symptoms become more severe. Even when people do present at an emergency department, it generally takes over 24 hours for their information to be collected, diagnosed and transferred for analysis at a centralized location. The advantage of utilizing tweets is that they offer information on syndromes in real-time. This paper investigates the value of carrying out multivariate syndromic surveillance using daily counts of keywords. The dynamic bi-plot is used to detect unexpected changes in the daily counts. These methods can be easily generalized to hourly tweet syndromic counts. By following Twitter users that suffer certain symptoms over time we can better understand the burden of these health issues and better understand emerging health issues. Monitoring people who present with symptoms but are just not sick enough to go to emergency departments provides us with additional information not gathered by emergency departments.