A model for characterizing annual flu cases

Miriam A Nuno, Marcello Pagano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Influenza outbreaks occur seasonally and peak during winter season in temperate zones of the Northern and Southern hemisphere. The occurrence and recurrence of flu epidemics has been alluded to variability in mechanisms such temperature, climate, host contact and traveling patterns [4]. This work promotes a Gaussian-type regression model to study flu outbreak trends and predict new cases based on influenza-like-illness data for France (1985-2005). We show that the proposed models are appropriate descriptors of these outbreaks and can improve the surveillance of diseases such as flu. Our results show that limited data reduces our ability to predict unobserved cases. Based on laboratory surveillance data, we prototype each season according to the dominating virus (H3N2, H1N1, B) and show that high intensity outbreaks are correlated with early peak times. These findings are in accordance with the dynamics observed for influenza outbreaks in the US.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics
Subtitle of host publicationBiosurveillance - Second NSF Workshop, BioSurveillance 2007, Proceedings
Number of pages10
Volume4506 LNCS
StatePublished - Dec 24 2007
Externally publishedYes
Event2nd NSF BioSurveillance Workshop, BioSurveillance 2007 - New Brunswick, NJ, United States
Duration: May 22 2007May 22 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd NSF BioSurveillance Workshop, BioSurveillance 2007
Country/TerritoryUnited States
CityNew Brunswick, NJ

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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