Epidemic cycling in a multi-strain SIRS epidemic network model

Xu-Sheng Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: One common observation in infectious diseases caused by multi-strain pathogens is that both the incidence of all infections and the relative fraction of infection with each strain oscillate with time (i.e., so-called Epidemic cycling). Many different mechanisms have been proposed for the pervasive nature of epidemic cycling. Nevertheless, the two facts that people contact each other through a network rather than following a simple mass-action law and most infectious diseases involve multiple strains have not been considered together for their influence on the epidemic cycling. Methods: To demonstrate how the structural contacts among people influences the dynamical patterns of multi-strain pathogens, we investigate a two strain epidemic model in a network where every individual randomly contacts with a fixed number of other individuals. The standard pair approximation is applied to describe the changing numbers of individuals in different infection states and contact pairs. Results: We show that spatial correlation due to contact network and interactions between strains through both ecological interference and immune response interact to generate epidemic cycling. Compared to one strain epidemic model, the two strain model presented here can generate epidemic cycling within a much wider parameter range that covers many infectious diseases. Conclusion: Our results suggest that co-circulation of multiple strains within a contact network provides an explanation for epidemic cycling.

Original languageEnglish
Article number14
JournalTheoretical Biology and Medical Modelling
Volume13
Issue number1
DOIs
Publication statusPublished - 18 Apr 2016

Bibliographical note

Publisher Copyright:
© 2016 Zhang.

Keywords

  • Competition
  • Contact network
  • Cross-immunity
  • Cyclical dominance of strains
  • Infectious diseases
  • Oscillatory epidemics

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