Comparison of deterministic and stochastic SIS and SIR models in discrete time

Linda J.S. Allen, Amy M. Burgin

Research output: Contribution to journalArticlepeer-review

203 Scopus citations

Abstract

The dynamics of deterministic and stochastic discrete-time epidemic models are analyzed and compared. The discrete-time stochastic models are Markov chains, approximations to the continuous-time models. Models of SIS and SIR type with constant population size and general force of infection are analyzed, then a more general SIS model with variable population size is analyzed. In the deterministic models, the value of the basic reproductive number R0 determines persistence or extinction of the disease. If R0 < 1, the disease is eliminated, whereas if R0 > 1, the disease persists in the population. Since all stochastic models considered in this paper have finite state spaces with at least one absorbing state, ultimate disease extinction is certain regardless of the value of R0. However, in some cases, the time until disease extinction may be very long. In these cases, if the probability distribution is conditioned on non-extinction, then when R0 > 1, there exists a quasi-stationary probability distribution whose mean agrees with deterministic endemic equilibrium. The expected duration of the epidemic is investigated numerically.

Original languageEnglish
Pages (from-to)1-33
Number of pages33
JournalMathematical Biosciences
Volume163
Issue number1
DOIs
StatePublished - Jan 2000

Keywords

  • Epidemic
  • Markov process
  • Quasi-stationary
  • Stochastic

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