Early and Real-Time Detection of Seasonal Influenza Onset

Miguel Won, Manuel Marques-Pita, Carlota Louro, Joana Gonçalves-Sá

Research output: Contribution to journalArticle

7 Citations (Scopus)
7 Downloads (Pure)

Abstract

Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We present a method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.

Original languageEnglish
Article numbere1005330
JournalPLoS Computational Biology
Volume13
Issue number2
DOIs
Publication statusPublished - 3 Feb 2017

Keywords

  • MODELS
  • EPIDEMICS

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