A Model for Characterizing Annual Flu Cases

  • Miriam Nuño
  • Marcello Pagano
Conference paper

DOI: 10.1007/978-3-540-72608-1_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)
Cite this paper as:
Nuño M., Pagano M. (2007) A Model for Characterizing Annual Flu Cases. In: Zeng D. et al. (eds) Intelligence and Security Informatics: Biosurveillance. Lecture Notes in Computer Science, vol 4506. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Miriam Nuño
    • 1
  • Marcello Pagano
    • 1
  1. 1.Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115USA

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