Long-Term Influenza Outbreak Forecast Using Time-Precedence Correlation of Web Data

Beakcheol Jang, Inhwan Kim, Jong Wook Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Influenza leads to many deaths every year and is a threat to human health. For effective prevention, traditional national-scale statistical surveillance systems have been developed, and numerous studies have been conducted to predict influenza outbreaks using web data. Most studies have captured the short-term signs of influenza outbreaks, such as one-week prediction using the characteristics of web data uploaded in real time; however, long-term predictions of more than 2-10 weeks are required to effectively cope with influenza outbreaks. In this study, we determined that web data uploaded in real time have a time-precedence relationship with influenza outbreaks. For example, a few weeks before an influenza pandemic, the word 'colds' appears frequently in web data. The web data after the appearance of the word 'colds' can be used as information for forecasting future influenza outbreaks, which can improve long-term influenza prediction accuracy. In this study, we propose a novel long-term influenza outbreak forecast model utilizing the time precedence between the emergence of web data and an influenza outbreak. Based on the proposed model, we conducted experiments on: 1) selecting suitable web data for long-term influenza prediction; 2) determining whether the proposed model is regionally dependent; and 3) evaluating the accuracy according to the prediction timeframe. The proposed model showed a correlation of 0.87 in the long-term prediction of ten weeks while significantly outperforming other state-of-the-art methods.

Original languageEnglish
Pages (from-to)2400-2412
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume34
Issue number5
DOIs
Publication statusPublished - 2023 May 1

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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