Nest architecture, activity pattern, worker density and the dynamics of disease transmission in social insects

Marcio R. Pie*, Rebeca B. Rosengaus, James F.A. Traniello

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

73 Citations (Scopus)


The role of disease in the organization of insect colonies has become an important focus of research in evolutionary pathobiology, in which the relationship of sociality and disease transmission can be comparatively and experimentally analysed. In this paper we use an individual-based model of disease transmission to assess how an epidemic is influenced by worker density and activity level, the probability of disease transmission, and the structural organization of the nest. First, we observed in our model a nonlinear interaction between worker density and the probability of disease transmission: high levels of both factors interact to enhance the likelihood of an epidemic. Additionally, when we incorporated in our model the empirical observation that only a fraction of the worker population in social insect colonies is active at any given point in time, results suggested that relatively low levels of worker movement can have a significant impact on the spread of disease, slowing its transmission through the colony. Finally, we found that nests having even a simple spatial separation of chambers could delay the spread of infection and diminish the severity of an outbreak. The effect of nest structure in delaying infection spread became more pronounced as nest architecture became increasingly unidimensional, as in the case of simple gallery nests. Therefore, nest architecture and worker activity patterns might indeed exert considerable influence on the dynamics of epidemics in social insects and should be incorporated into models of disease transmission.

Original languageEnglish
Pages (from-to)45-51
Number of pages7
JournalJournal of Theoretical Biology
Issue number1
Publication statusPublished - 7 Jan 2004


  • Agent-based modeling
  • Epizootic
  • Infection


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