Detecting and mapping real-time Influenza-like illness using Twitter stream data

<p>Influenza has been identified by the World Health Organization as a global issue that could be more effectively served through an accelerated and widely-accessible public health surveillance tracking process. The ability to map and predict influenza outbreaks in a real-time heat map would be invaluable to health care systems to prepare for influenza outbreaks. In this study, the Twitter stream data is filtered to identify potential influenza-like illness (ILI) cases. Then the tracking of real-time influenza cases is further explored and analyzed through various machine-learning models. Among seven learning models developed to identify ILI tweets, the ELMo deep neural network model outperforms others regarding model accuracy and F-score. A heat map is generated to visualize real-time outbreaks of ILI in the U.S.A.</p>

Name thesis:degree_name

Master of Science (MS) in Computer Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2019

Author dc:creator

• Brunette, Elisha D

• Community Health and Preventive Medicine
• Influenza Humans
• Other Computer Sciences

Statement dc:rights

• Access is available to all users

Repository record dc:identifier

https://dc.ewu.edu/theses/600

OAI identifier oai:identifier

oai:dc.ewu.edu:theses-1596