Influenza Outbreak Event Prediction via Twitter

Donated on 8/14/2023

By identifying influenza-related tweets, the goal is to forecast the spatiotemporal patterns of influenza outbreaks for different locations and dates.

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real, Integer

# Instances

75839

# Features

523

Dataset Information

Additional Information

The data is from the United States. The data comes from different states under different weeks. For each week, the task is to predict whether or not there is an influenza outbreak on the next date. More specifically, for influenza activity, there are four levels of flu activities from minimal to high according to CDC Flu Activity Map. An influenza outbreak occurrence is indicated if the activity level is high.

Has Missing Values?

No

Introductory Paper

SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning

By Liang Zhao, Jiangzhuo Chen, F. Chen, W. Wang, Chang-Tien Lu, Naren Ramakrishnan. 2015

Published in 2015 IEEE International Conference on Data Mining

Variable Information

The input of the prediction task is the set of the keyword counts for all the tweets in a state in a week. The output is the occurrence of influenza outbreak for the specific state in the next week, which is zero if no event in the next week; or one, otherwise. Here are the briefs of all the variables: 'flu_locations': a list of states. 'flu_keywords': keyword list. 'flu_X_*': input data for all the locations and all the weeks. 'flu_Y_*': output data for all the locations and all the weeks. 525 keywords specified in the variable 'flu_keywords' in the data

Dataset Files

FileSize
influenza_outbreak_dataset.mat4.6 MB

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download (4.3 MB)
1 citations
6183 views

Keywords

Creators

Liang Zhao

liang.zhao@emory.edu

Emory University

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy