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Abel-Gontcharoff pseudopolynomials and the exact final outcome of SIR epidemic models (III)

Published online by Cambridge University Press:  01 July 2016

Claude Lefèvre*
Affiliation:
Université Libre de Bruxelles
Philippe Picard*
Affiliation:
Université de Lyon 1
*
Postal address: Institut de Statistique et de Recherche Opérationnelle, Université Libre de Bruxelles, C.P. 210, Boulevard du Triomphe, B-1050 Bruxelles, Belgique. Email address: [email protected]
∗∗ Postal address: Mathématiques Appliquées, Université de Lyon 1, 43 boulevard du 11 Novembre 1918, F-69622 Villeurbanne Cedex, France. Email address: [email protected]

Abstract

The paper is concerned with the final state and severity of a number of SIR epidemic models in finite populations. Two different classes of models are considered, namely the classical SIR Markovian models and the collective models introduced recently by the authors. First, by applying a simple martingale argument, it is shown that in both cases, there exists a common algebraic structure underlying the exact law of the final state and severity. Then, a unified approach to these statistics is developed by exploiting the theory of Abel-Gontcharoff pseudopolynomials (presented in a preceding paper).

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 1999 

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References

Ball, F. G. and Clancy, D. (1993). The final size and severity of a generalised stochastic multitype epidemic model. Adv. Appl. Prob. 25, 721736.Google Scholar
Ball, F. G. and O'Neill, P. D. (1993). A modification of the general stochastic epidemic motivated by AIDS modelling. Adv. Appl. Prob. 25, 3962.Google Scholar
Ball, F. G. and O'Neill, P. D. (1997). The distribution of general final state random variables for stochastic epidemic models. Research Report 97-05, Nottingham University Statistics Group.Google Scholar
Ball, F.G., Mollison, D. and Scalia-Tomba, G. (1997). Epidemics with two levels of mixing. Ann. Appl. Prob. 7, 4689.CrossRefGoogle Scholar
Gani, J. and Jerwood, D. (1972). The cost of a general stochastic epidemic. J. Appl. Prob. 9, 257269.CrossRefGoogle Scholar
Gani, J. and Purdue, P. (1984). Matrix-geometric methods for the general stochastic epidemic. IMA J. Math. Appl. Med. Biol. 1, 333342.CrossRefGoogle ScholarPubMed
Lefèvre, C., (1990). Stochastic epidemic models for S-I-R infectious diseases: a brief survey of the recent general theory. In Stochastic Processes in Epidemic Theory (Lecture Notes Biomath. 86), ed. Gabriel, J.-P., C. Lefèvre and P. Picard. Springer, Heidelberg, pp. 112.Google Scholar
Lefèvre, C. and Picard, P. (1990). A non standard family of polynomials and the final size distribution of Reed-Frost epidemic processes. Adv. Appl. Prob. 22, 2548.CrossRefGoogle Scholar
Lefèvre, C. and Picard, P. (1994). The exact distribution of the outcome of a rumour process. J. Appl. Prob. 31, 244249.CrossRefGoogle Scholar
Lefèvre, C. and Picard, P. (1996a). Collective epidemic models. Math. Biosci. 134, 5170.CrossRefGoogle ScholarPubMed
Lefèvre, C. and Picard, P. (1996b). Abelian-type expansions and non-linear death processes (II). Adv. Appl. Prob. 28, 877894.CrossRefGoogle Scholar
Ludwig, D. (1975). Final size distributions for epidemics. Math. Biosci. 23, 3346.CrossRefGoogle Scholar
Martin-Lof, A., (1986). Symmetric sampling procedures, general epidemic processes and their threshold limit theorems. J. Appl. Prob. 23, 265282.CrossRefGoogle Scholar
O'Neill, P. D. (1997). An epidemic model with removal-dependent infection rate. Ann. Appl. Prob. 7, 90109.Google Scholar
Picard, P. (1980). Applications of martingale theory to some epidemic models. J. Appl. Prob. 17, 583599.CrossRefGoogle Scholar
Picard, P. (1984). Applications of martingale theory to some epidemic models, II. J. Appl. Prob. 21, 677684.Google Scholar
Picard, P. and Lefèvre, C. (1990). A unified analysis of the final size and severity distribution in collective Reed-Frost epidemic processes. Adv. Appl. Prob. 22, 269294.Google Scholar
Picard, P. and Lefèvre, C. (1991). The dimension of Reed-Frost epidemic models with randomized susceptibility levels. Math. Biosci. 107, 225233.Google Scholar
Picard, P. and Lefèvre, C. (1993). Distribution of the final state and severity of epidemics with fatal risk. Stoch. Proc. Appl. 48, 277294.CrossRefGoogle Scholar
Picard, P. and Lefèvre, C. (1996). First crossing of basic counting processes with lower non-linear boundaries: a unified approach through pseudopolynomials (I). Adv. Appl. Prob. 28, 853876.CrossRefGoogle Scholar
Scalia-Tomba, G. (1990). On the asymptotic final size distribution of epidemics in heterogeneous populations. In Stochastic Processes in Epidemic Theory (Lecture Notes Biomath. 86), ed. Gabriel, J.-P., C. Lefèvre and P. Picard. Springer, Heidelberg, pp. 189196.Google Scholar