Date of Award
Fall 2019
Rights
Access is available to all users
Date Available to Non-EWU Users
2020-12-12
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
