Two-type branching processes with subexponential life-spans and SIR epidemic models

I. Rahimov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We consider a model of age-dependent branching stochastic process that takes into account the incubation period of the life of individuals. We demonstrate that such processes may be treated as a two-type branching process with a periodic mean matrix. In the case when the Malthusian parameter does not exist study of the process requires additional restrictions on the life and incubation time distributions which define so called subexponential family (Athreya, K. 1972. Branching Processes, Springer, New York). We obtain certain new properties of subexponential distributions, in particular, describe a subclass, which is closed with respect to convolution. Using these results we derive asymptotic behavior of the first and second moments and of the probability of nonextinction. We also prove a limit theorem for the process conditioned on nonextinction.

Original languageEnglish
Pages (from-to)925-940
Number of pages16
JournalStochastic Analysis and Applications
Volume26
Issue number5
DOIs
StatePublished - Sep 2008

Bibliographical note

Funding Information:
Received February 14, 2007; Accepted October 4, 2007 These results are part of the project No FT-2006/03 funded by KFUPM, Dhahran, Saudi Arabia. The author is indebted to King Fahd University of Petroleum and Minerals for excellent research facilities. He is also grateful to the referee and one of editors for valuable comments on the first version of the article.

Keywords

  • Branching process
  • Epidemic
  • Extinction
  • Incubation period
  • Malthusian parameter
  • Subexponential class

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Two-type branching processes with subexponential life-spans and SIR epidemic models'. Together they form a unique fingerprint.

Cite this