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Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease

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  • 1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, United States.

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Summary
This summary is machine-generated.

This study introduces a novel framework using social media data for efficient and accurate disease outbreak tracking and prediction, even for new diseases. The system leverages FastText classification and linear regression for real-time flu trend analysis.

Keywords:
influenzainfodemiologymachine learningsocial networking sitetext classification

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Area of Science:

  • Computational epidemiology
  • Public health surveillance
  • Machine learning applications

Background:

  • Social networking sites (SNSs) offer vast real-time data for disease outbreak tracking.
  • Conventional machine learning methods are insufficient for novel outbreaks with evolving symptoms.

Purpose of the Study:

  • To develop an efficient and accurate framework for tracking disease outbreaks using SNS data.
  • To provide early warnings for emerging infectious diseases, including novel ones.

Main Methods:

  • A 3-module framework: text classification (FastText vs. ML), mapping, and linear regression.
  • FastText (FT) classifier evaluated for efficiency and accuracy in classifying flu-related tweets.
  • Weekly flu rate predictions generated using mapped tweet data and historical CDC data.

Main Results:

  • FastText achieved 89.9% F-measure for flu tweet classification, proving efficient and accurate.
  • Linear regression model demonstrated high accuracy (96.29% correlation with CDC data) for weekly flu rate prediction.
  • The framework successfully predicted flu trends with high correlation to ground truth data.

Conclusions:

  • The proposed FT-based framework enhances accuracy and efficiency in disease surveillance using SNS data.
  • The system is effective for tracking and predicting new outbreaks with novel symptoms.
  • This approach offers a valuable tool for real-time public health monitoring.