Modelling seasonal variations in the age and incidence of Kawasaki disease to explore possible infectious aetiologies

Virginia E. Pitzer, David Burgner, Cécile Viboud, Lone Simonsen, Viggo Andreasen, Claudia A. Steiner, Marc Lipsitch

Research output: Contribution to journalArticlepeer-review

Abstract

The average age of infection is expected to vary during seasonal epidemics in a way that is predictable from the epidemiological features, such as the duration of infectiousness and the nature of population mixing. However, it is not known whether such changes can be detected and verified using routinely collected data. We examined the correlation between the weekly number and average age of cases using data on pre-vaccination measles and rotavirus. We show that age-incidence patterns can be observed and predicted for these childhood infections. Incorporating additional information about important features of the transmission dynamics improves the correspondence between model predictions and empirical data. We then explored whether knowledge of the age-incidence pattern can shed light on the epidemiological features of diseases of unknown aetiology, such as Kawasaki disease (KD). Our results indicate KD is unlikely to be triggered by a single acute immunizing infection, but is consistent with an infection of longer duration, a non-immunizing infection or co-infection with an acute agent and one with longer duration. Age-incidence patterns can lend insight into important epidemiological features of infections, providing information on transmission-relevant population mixing for known infections and clues about the aetiology of complex paediatric diseases.

Original languageEnglish (US)
Pages (from-to)2736-2743
Number of pages8
JournalProceedings of the Royal Society B: Biological Sciences
Volume279
Issue number1739
DOIs
StatePublished - Jul 22 2012

Keywords

  • Infectious disease dynamics
  • Mathematical modelling
  • Seasonality

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

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