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Coupling a branching process to an infinite dimensional epidemic process* **

Published online by Cambridge University Press:  15 December 2008

Andrew D. Barbour*
Affiliation:
Universität Zürich Angewandte Mathematik, Winterthurerstrasse 190, 8057 Zürich, Switzerland
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Abstract

Branching process approximation to the initial stages of an epidemicprocess has been used since the 1950's as a technique for providing stochastic counterparts to deterministic epidemic threshold theorems.One way of describing the approximation is to construct both branching and epidemic processes on the same probability space, insuch a way that their paths coincide for as long as possible. Inthis paper, it is shown, in the context of a Markovian model of parasiticinfection, that coincidence can be achieved with asymptotically high probability until MN infections have occurred, as long asMN = o(N 2/3), where N denotes the total number of hosts.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2010

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Footnotes

*

Work supported in part by Schweizerischer Nationalfonds Projekt No. 20–117625/1.

**

To Cindy Greenwood, for her 70th.

References

Ball, F.G., The threshold behaviour of epidemic models. J. Appl. Probab. 20 (1983) 227241. CrossRef
Ball, F.G. and Donnelly, P., Strong approximations for epidemic models. Stoch. Proc. Appl. 55 (1995) 121. CrossRef
Barbour, A.D. and Kafetzaki, M., A host–parasite model yielding heterogeneous parasite loads. J. Math. Biol. 31 (1993) 157176. CrossRef
Barbour, A.D. and Utev, S., Approximating the Reed-Frost epidemic process. Stoch. Proc. Appl. 113 (2004) 173197. CrossRef
M.S. Bartlett, An introduction to stochastic processes. Cambridge University Press (1956).
O. Diekmann and J.A.P. Heesterbeek, Mathematical epidemiology of infectious diseases. Wiley, New York (2000).
J.A.P. Heesterbeek, R0. CWI, Amsterdam (1992).
Kendall, D.G., Deterministic and stochastic epidemics in closed populations. Proc. Third Berk. Symp. Math. Stat. Probab. 4 (1956) 149165.
Kurtz, T.G., Limit theorems and diffusion approximations for density dependent Markov chains. Math. Prog. Study 5 (1976) 6778. CrossRef
T.G. Kurtz, Approximation of population processes, volume 36 of CBMS-NSF Regional Conf. Series in Appl. Math. SIAM, Philadelphia (1981).
Luchsinger, C.J., Stochastic models of a parasitic infection, exhibiting three basic reproduction ratios. J. Math. Biol. 42 (2002) 532554. CrossRef
Luchsinger, C.J., Approximating the long-term behaviour of a model for parasitic infection. J. Math. Biol. 42 (2002) 555581. CrossRef
Whittle, P., The outcome of a stochastic epidemic – a note on Bailey's paper. Biometrika 42 (1955) 116122.