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Applications of branching processes to the final size of SIR epidemics

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Workshop on Branching Processes and Their Applications

Part of the book series: Lecture Notes in Statistics ((LNSP,volume 197))

Abstract

This paper considers applications of branching processes to a model for the spread of an SIR (susceptible \(\to\) infective \(\to\) removed) epidemic among a closed, homogeneously mixing population, consisting initially of m infective and n susceptible individuals. Each infective remains infectious for a period sampled independently from an arbitrary but specified distribution, during which he/she contacts susceptible individuals independently with rate \(n^{-1}\lambda\) for each susceptible. The well-known approximation of the early stages of this epidemic model by a branching process is outlined. The main thrust of the paper is to use branching processes to obtain, when the infectious period is constant, new and probabilistically direct proofs of central limit theorems for the size of an epidemic which becomes established. Two asymptotic situations are considered: (i) many initial infectives, where m and n both become large, for which establishment is asymptotically certain; and (ii) few initial infectives, where m is held fixed and only n becomes large, for which asymptotically establishment is not certain and may not be possible. The model with constant infectious periods is closely related to the Erdös-Réenyi random graph and our methodology provides an alternative proof of the central limit theorem for the size of the giant component in that graph.

Mathematics Subject Classification (2000): 92D30, 60J80, 60F05, 05C80

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References

  1. Aldous, D.J.: Exchangeability and related topics. In: Hennequin, P.L. (ed.) Ecole d’Été de Probabilités de Saint-Flour XIII, 1993 (Lecture Notes in Mathematics 1117), pp. 1–198. Springer, Berlin (1985)

    Google Scholar 

  2. Andersson, H., Britton, T.: Stochastic Epidemic Models and Their Statistical Analysis (Lecture Notes in Statistics 151). Springer, New York (2000)

    Google Scholar 

  3. von Bahr, B., Martin-Löf, A.: Threshold limit theorems for some epidemic processes. Adv. Appl. Probab. 12, 319–349 (1980)

    Article  MATH  Google Scholar 

  4. Bailey, N.T.J.: The Mathematical Theory of Infectious Diseases and its Applications. Griffin, London (1975)

    Google Scholar 

  5. Ball, F.: The threshold behaviour of epidemic models. J. Appl. Probab. 20, 227–241 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  6. Ball, F., Donnelly, P.: Strong approximations for epidemic models. Stoch. Proc. Appl. 55, 1–21 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  7. Ball, F., Lyne, O.: Stochastic multitype SIR epidemics among a population partitioned into households. Adv. Appl. Probab.33, 99–123 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  8. Ball, F., Neal, P.: A general model for stochastic SIR epidemics with two levels of mixing. Math. Biosci. 180, 73–102 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Ball, F., Neal, P.: Network epidemic models with two levels of mixing. Math. Biosci. 212, 69–87 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  10. Ball, F., Neal, P.: Epidemics upon random graphs with two levels of mixing. In preparation.

    Google Scholar 

  11. Ball, F., Neal, P.: Central limit theorems for the final size of collective Reed–Frost epidemics. In preparation.

    Google Scholar 

  12. Barbour, A.D.: On a functional central limit theorem for Markov population processes. Adv. Appl. Probab. 6, 21–39 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  13. Barbour, A., Mollison, D.: Epidemics and random graphs. In: Gabriel, J.-P., Lefèvre, C., Picard, P. (eds.) Stochastic Processes in Epidemic Theory (Lecture Notes in Biomathematics 86), pp. 86–89. Springer, New York (1990)

    Google Scholar 

  14. Barbour, A.D., Utev, S.: Approximating the Reed–Frost epidemic process.Stoch. Proc. Appl. 113, 173–197 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  15. Barrez, D., Boucheron, S., Fernandez de la Vega, W.: On the fluctuations of the giant component. Comb. Probab. Comput. 9, 287–304.

    Google Scholar 

  16. Bartlett, M.S.: An Introduction to Stochastic Processes. Cambridge University Press, Cambridge (1955)

    Google Scholar 

  17. Daly, F.: Collapsing supercritical branching processes. J. Appl. Probab. 16, 732–739 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  18. Durrett, R. Random Graph Dynamics. Cambridge University Press, Cambridge (2007)

    Google Scholar 

  19. Dwass, M.: The total progeny in a branching process and a related random walk. J. Appl. Probab. 6, 682–686 (1969)

    Article  MATH  MathSciNet  Google Scholar 

  20. Esary, J.D., Proschan, F., Walkup, D.W.: Association of random variables with applications.Ann. Math. Stat. 38, 1466–1474 (1967)

    Article  MATH  MathSciNet  Google Scholar 

  21. Ethier, S.N., Kurtz, T.G.: Markov Processes:Characterization and Convergence. Wiley, New York (1986)

    Google Scholar 

  22. Haccou, P., Jagers, P, Vatutin,V.A.: Branching Processes: Variation, Growth, and Extinction of Populations. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  23. Heesterbeek, J.A.P., Dietz, K.: The concept of R 0 in epidemic theory. Stat. Neerl. 50, 89–110 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  24. Jagers, P.: Branching Processes with Biological Applications. Wiley, Chichester (1975)

    Google Scholar 

  25. Kendall, D.G.: Deterministic and stochastic epidemics in closed populations. Proc. 3rd Berkeley Symp. Math. Statist. Probab. 4, 149–165 (1956)

    MathSciNet  Google Scholar 

  26. Martin-Löf, A.: Symmetric sampling procedures, general epidemic processes and their threshold limit theorems. J. Appl. Probab.23, 265–282 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  27. Metz, J.A.J.: The epidemic in a closed population with all susceptibles equally vulnerable; some results for large susceptible populations and small initial infections. Acta Biotheoretica 27, 75–123 (1978)

    Article  Google Scholar 

  28. Nagaev, A.V., Startsev, A.N.: The asymptotic analysis of a stochastic model of an epidemic. Theory Probab. Appl. 15, 98–107 (1970)

    Article  MATH  Google Scholar 

  29. Peligrad, M., Utev, S.: Central limit theorem for linear processes. Ann. Probab. 25, 443–456 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  30. Pellis, L., Ferguson, N.M., Fraser, C.: The relationship between real-time and discrete-generation models of epidemic spread. Math. Biosci. 216, 63–70 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  31. Picard, P., Lefèvre, C: A unified analysis of the final size and severity distribution in collective Reed–Frost epidemic processes. Adv. Appl. Probab. 22, 269–294 (1990)

    Article  MATH  Google Scholar 

  32. Pittel, B.: On tree census and the giant component in sparse random graphs. Random Struct. Alg. 1, 311–342 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  33. Scalia-Tomba, G.-P.: Asymptotic final size distribution for some chain-binomial models. Adv. Appl. Probab. 17, 477–495 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  34. Scalia-Tomba, G.-P.: On the asymptotic final size distribution of epidemics in heterogeneous populations. In: Gabriel, J.-P., Lefèvre, C., Picard, P. (eds.) Stochastic Processes in Epidemic Theory (Lecture Notes in Biomathematics 86), pp. 189–196. Springer, New York (1990)

    Google Scholar 

  35. Watson, R.: A useful random time scale transformation for the standard epidemic model. J. Appl. Probab. 17, 324–332 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  36. Watson, R.: An application of a martingale central limit theorem to the standard epidemic model. Stoch. Proc. Appl. 11, .79–89 (1981)

    Article  MATH  Google Scholar 

  37. Whittle, P.: The outcome of a stochastic epidemic – a note on Bailey’s paper. Biometrika 42, 116–122 (1955)

    MATH  MathSciNet  Google Scholar 

  38. Williams, T.: An algebraic proof of the threshold theorem for the general stochastic epidemic. Adv. Appl. Probab. 3, 223 (1971)

    Article  Google Scholar 

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Acknowledgements

We thank Sergey Utev for helpful discussions concerning Sect. 15.3.4 and the referee for a very careful reading of our paper. Frank Ball was supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) (Grant No. EP/E038670/1).

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Correspondence to Frank Ball or Peter Neal .

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Ball, F., Neal, P. (2010). Applications of branching processes to the final size of SIR epidemics. In: González Velasco, M., Puerto, I., Martínez, R., Molina, M., Mota, M., Ramos, A. (eds) Workshop on Branching Processes and Their Applications. Lecture Notes in Statistics(), vol 197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11156-3_15

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