Generality of endemic prevalence formulae

Damian Clancy*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
87 Downloads (Pure)

Abstract

In simple infection models, the susceptible proportion s* in endemic equilibrium is related to the basic reproduction number R0 by s* = 1/R0. We investigate the extent to which this relationship remains valid under more realistic modelling assumptions. In particular, we relax the biologically implausible assumptions that individuals' lifetimes and infectious periods follow exponential distributions; allow a general recruitment process; allow for multiple stages of infection; and consider extension to a multigroup model in which the groups may represent, for instance, spatial heterogeneity, or the existence of super-spreaders. For a homogeneous population, we find that: (i) the susceptible proportion is s* = 1/R0e, where R0e is a modified reproduction number, equal to R0 only in certain circumstances; (ii) the proportions of the population in each stage of infection are proportional to the expected time spent by an infected individual in that stage before recovery or death. We demonstrate robustness of the formula s* = 1/R0 for many human infections by noting conditions under which R0e is approximately equal to R0, while pointing out other circumstances under which this approximation fails. For heterogeneous populations, the formula s* = 1/R0 does not hold in general, but we are able to exhibit symmetry conditions under which it is valid.

Original languageEnglish
Pages (from-to)30-36
Number of pages7
JournalMathematical Biosciences
Volume269
DOIs
Publication statusPublished - Nov 2015

Keywords

  • Basic reproduction number
  • Endemic equilibrium
  • Spatial heterogeneity
  • Super-spreaders

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modelling and Simulation
  • General Agricultural and Biological Sciences
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Medicine

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