Date of Award
Fall 2019
Rights
Access is available to all users
Document Type
Thesis
Degree Name
Master of Science (MS) in Computer Science
Department
Computer Science
Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Brunette, Elisha D., "Detecting and mapping real-time Influenza-like illness using Twitter stream data" (2019). EWU Masters Thesis Collection. 600.
https://dc.ewu.edu/theses/600
Included in
Community Health and Preventive Medicine Commons, Influenza Humans Commons, Other Computer Sciences Commons
