Articles

ON THE BASIC REPRODUCTION NUMBER OF GENERAL BRANCHING PROCESSES

  • LA Guo-Lie ,
  • MA Zhi-Meng ,
  • SUN Su-Yong
Expand
  • 1.School of Mathematics and information Sciences, Guangzhou University, Guangzhou 510006, China;
    2.Inst. Appl. Math., Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;3.Graduate University of the Chinese Academy of Sciences

Received date: 2007-03-27

  Online published: 2009-07-20

Supported by

This work is supported in part by NSFC and 973 Project

Abstract

Under a very general condition (TNC condition) we show that the spectral radius of the kernel of a general branching process is a threshold parameter and hence plays a role as the basic reproduction number in usual CMJ processes. We discuss also some properties of the extinction probability and the generating operator of general branching processes. As an application in epidemics, in the final section we suggest a generalization
of SIR model which can describe infectious diseases transmission in an inhomogeneous population.

Cite this article

LA Guo-Lie , MA Zhi-Meng , SUN Su-Yong . ON THE BASIC REPRODUCTION NUMBER OF GENERAL BRANCHING PROCESSES[J]. Acta mathematica scientia, Series B, 2009 , 29(4) : 1081 -1094 . DOI: 10.1016/S0252-9602(09)60087-4

References


[1] Bailey N T J. The Mathematical Theory of Infectious Diease and Its Applications. Second Edition. The Griffin & Company Ltd, 1975


[2] Gong F, Wu L. Spectral gap of positive operators and applications. J Math Pures Appl, 2006, 85: 151–191


[3] Gong F, Wu L. Essential spectral radius for positive operators on L1 and L1 spaces. Preprint
[4] Harris T E. The Theory of Branching Processes. Berlin: Springer, 1963


[5] Hennion H. Quasi-compactness and Absolutely Continuous Kernels. Probability Theory and Related Fields, 2007, 139: 451–471


[6] Jagers P. Branching Process with Biological Applications. London, New York: John Wiley & Sons, 1975


[7] Jagers P. General branching processes as Markov fields. Stochastic Processes and Their Applications,
1989, 32: 183–212


[8] Jagers P, Sagitov S. General branching processes in discrete time as random trees. Bernoulli, 2008, 14:
949–962


[9] Kallenberg O. Random Measurable. Berlin: Akademie-Verlag, London: Academic Press, 1983


[10] Karr A F. Point Processes and Their Statistical Inference. New York: Marcel, 1991


[11] Lan G. Stochastic Epidemic Models Based on Point Processes
[D]. Beijing: Graduate University of the
Chinese Academy of Sciences, 2006


[12] Meyer-Nieberg P. Banach Lattices. Berlin, New York: Springer-Verlag, 1991


[13] Mode C, Sleeman C. Stochastic Processes in Epidemiology, HIV/AIDS, Other Infectious Diseases and
Computers. Singapore: World Scientific, 2000


[14] Revus D. Markov Chain. Amsterdam: North-Holland, 1976


[15] Yan P. Separate roles of the latent and infectious periods in shaping the relation between the basic re-
production number and the intrinsic growth rate of infectious disease outbreaks. Journal of Theoretical Biology, 2008, 251: 238–252


[16] Yan P. Distribution theory, stochastic processes and infectious disease modelling. to appear in Lecture
Notes in Mathematics/Mathematical Biosciences Subseries). Springer (Paperback-Jun 6, 2008)


[17] Wu L M. Uniformly integrable operators and large deviations for markov process. J Funct Anal, 2000,
172: 301–376

Outlines

/