Skip to main content
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2010 Apr 28;62(4):479–508. doi: 10.1007/s00285-010-0344-x

Exact epidemic models on graphs using graph-automorphism driven lumping

Péter L Simon 1, Michael Taylor 2, Istvan Z Kiss 2,
PMCID: PMC7079990  PMID: 20425114

Abstract

The dynamics of disease transmission strongly depends on the properties of the population contact network. Pair-approximation models and individual-based network simulation have been used extensively to model contact networks with non-trivial properties. In this paper, using a continuous time Markov chain, we start from the exact formulation of a simple epidemic model on an arbitrary contact network and rigorously derive and prove some known results that were previously mainly justified based on some biological hypotheses. The main result of the paper is the illustration of the link between graph automorphisms and the process of lumping whereby the number of equations in a system of linear differential equations can be significantly reduced. The main advantage of lumping is that the simplified lumped system is not an approximation of the original system but rather an exact version of this. For a special class of graphs, we show how the lumped system can be obtained by using graph automorphisms. Finally, we discuss the advantages and possible applications of exact epidemic models and lumping.

Keywords: Network, Epidemic, Markov chain, Lumping, Graph automorphism

References

  1. Anderson RM, May RM (1991) Infectious diseases of humans: dynamics and control. Oxford University Press
  2. Andersson H, Djehich B. A threshold limit theorem for the stochastic logistic epidemic. J Appl Prob. 1997;35:662–670. [Google Scholar]
  3. Ball F, Mollison D, Salia-Tomba G. Epidemics with two levels of mixing. Ann Appl Prob. 1997;7:46–89. doi: 10.1214/aoap/1034625252. [DOI] [Google Scholar]
  4. Brandes U, Erlebach T. Network analysis: methodological foundations. Berlin: Springer-Verlag; 2005. [Google Scholar]
  5. Brauer F, van den Driessche P, Wu J. Mathematical epidemiology. In: Lecture notes in mathematics. Berlin: Springer-Verlag; 2008. [Google Scholar]
  6. Broom M, Rychtář J. An analysis of the fixation probability of a mutant on special classes of non-directed graphs. Proc R Soc A. 2008;464:2609–2627. doi: 10.1098/rspa.2008.0058. [DOI] [Google Scholar]
  7. Daley DJ, Gani J (1999) Epidemic modelling: an intoduction. Cambridge University Press
  8. Dent JE, Kao RR, Kiss IZ, Hyder K, Arnold M. Contact structures in the poultry industry in Great Britain: exploring transmission routes for a potential avian influenza virus epidemic. BMC Vet Res. 2008;4:27. doi: 10.1186/1746-6148-4-27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Diekmann O, Heesterbeek JAP. Mathematical epidemiology of infectious diseases: model building, analysis and interpretation. Chichester: Wiley; 2000. [Google Scholar]
  10. Diekmann O, De Jong MCM, Metz JAJ. A deterministic epidemic model taking account of repeated contacts between the same individuals. J Appl Prob. 1998;35:448–462. doi: 10.1239/jap/1032192860. [DOI] [Google Scholar]
  11. Ferguson NM, Donnelly CA, Anderson RM. The foot-and mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science. 2001;292:1155–1160. doi: 10.1126/science.1061020. [DOI] [PubMed] [Google Scholar]
  12. Filliger L, Hongler M-O (2008) Lumping complex networks. Madeira Math Encounters XXXV. http://ccm.uma.pt/mme35/files/LumpingNetworks-Filliger.pdf
  13. Green DM, Kiss IZ, Kao RR. Modelling the initial spread of foot-and-mouth disease through animal movements. Proc R Soc B. 2006;273:2729–2735. doi: 10.1098/rspb.2006.3648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gross JL, Yellen J, editors. Handbook of graph theory. Boca Raton: CRC Press; 2003. [Google Scholar]
  15. House T, Davies G, Danon L, Keeling MJ. A motif-based approach to network epidemics. Bull Math Biol. 2009;71:1693–1706. doi: 10.1007/s11538-009-9420-z. [DOI] [PubMed] [Google Scholar]
  16. Hufnagel L, Brockmann D, Geisel T. Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA. 2004;101:15124–15129. doi: 10.1073/pnas.0308344101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hundsdorfer W, Verwer JG. Numerical solution of time-dependent advection-diffusion-reactions equations. New York: Springer; 2003. [Google Scholar]
  18. Jacobi MN, Görnerup O. A spectral method for aggregating variables in linear dynamical systems with application to cellular automata renormalization. Adv Complex Syst. 2009;12:1–25. doi: 10.1142/S0219525909002155. [DOI] [Google Scholar]
  19. Kao RR, Danon L, Green DM, Kiss IZ. Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain. Proc R Soc B. 2006;273:1999–2007. doi: 10.1098/rspb.2006.3505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Keeling MJ. The effects of local spatial structure on epidemiological invasions. Proc R Soc Lond B. 1999;266:859–867. doi: 10.1098/rspb.1999.0716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Keeling MJ. The implications of network structure for epidemic dynamics. Theor Popul Biol. 2005;67:1–8. doi: 10.1016/j.tpb.2004.08.002. [DOI] [PubMed] [Google Scholar]
  22. Keeling MJ, Eames KTD. Networks and epidemic models. J R Soc Interface. 2005;2:295–307. doi: 10.1098/rsif.2005.0051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Keeling MJ, Ross JV. On methods for studying stochastic disease dynamics. J R Soc Interface. 2008;5:171–181. doi: 10.1098/rsif.2007.1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Keeling MJ, Rand DA, Morris AJ. Correlation models for childhood epidemics. Proc R Soc B. 1997;264:1149–1156. doi: 10.1098/rspb.1997.0159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kemeny JG, Snell JL. Finite Markov chains. 2. New York: Springer; 1976. [Google Scholar]
  26. Kenah E, Robins JM. Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing. J Theor Biol. 2007;249:706–722. doi: 10.1016/j.jtbi.2007.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kermack WO, McKendrick AG. A contribution to the mathematical study of epidemics. Proc R Soc Lond Ser A. 1927;115:700–721. doi: 10.1098/rspa.1927.0118. [DOI] [Google Scholar]
  28. Kiss IZ, Green DM, Kao RR. Disease contact tracing in random and clustered networks. Proc R Soc B. 2005;272:1407–1414. doi: 10.1098/rspb.2005.3092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kiss IZ, Green DM, Kao RR. The network of sheep movements within Great Britain: network properties and their implications for infectious disease spread. J R Soc Interface. 2006;3:669–677. doi: 10.1098/rsif.2006.0129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kiss IZ, Green DM, Kao RR. The effect of network heterogeneity and multiple routes of transmission on final epidemic size. Math Biosci. 2006;203:124–136. doi: 10.1016/j.mbs.2006.03.002. [DOI] [PubMed] [Google Scholar]
  31. Kiss IZ, Green DM, Kao RR. The effect of network mixing patterns on epidemic dynamics and the efficacy of disease contact tracing. J R Soc Interface. 2008;5:791–799. doi: 10.1098/rsif.2007.1272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kurtz TG. Solutions of ordinary differential equations as limits of pure jump Markov processes. J Appl Prob. 1970;7:49–58. doi: 10.2307/3212147. [DOI] [Google Scholar]
  33. Kurtz TG. Limit theorems for sequences of jump Markov processes approximating ordinary differential processes. J Appl Prob. 1971;8:344–356. doi: 10.2307/3211904. [DOI] [Google Scholar]
  34. Lipsitch M, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science. 2003;300:1966–1970. doi: 10.1126/science.1086616. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. May RM, Lloyd AL. Infection dynamics on scale-free networks. Phys Rev E. 2001;64:066112. doi: 10.1103/PhysRevE.64.066112. [DOI] [PubMed] [Google Scholar]
  36. Meyers LA, Pourbohloul B, Newman MEJ, Skowronski DM, Brunham RC. Network theory and SARS: predicting outbreak diversity. J Theor Biol. 2005;232:71–81. doi: 10.1016/j.jtbi.2004.07.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nåsell I. The quasi-stationary distribution of the closed endemic SIS model. Adv Appl Probab. 1996;28:895–932. doi: 10.2307/1428186. [DOI] [Google Scholar]
  38. Newman MEJ. The spread of epidemic disease on networks. Phys Rev E. 2002;66:016128. doi: 10.1103/PhysRevE.66.016128. [DOI] [PubMed] [Google Scholar]
  39. Picard P. Sur les modèles stochastique logistiques en démographie. Ann Inst Henri Poincaré B. 1965;II:151–172. [Google Scholar]
  40. Rand DA. Correlation equations for spatial ecologies. In: McGlade J, editor. Advanced ecological theory. Oxford: Blackwell; 1999. pp. 100–142. [Google Scholar]
  41. Sato K, Matsuda H, Sasaki A. Pathogen invasion and host extinction in lattice structured populations. J Math Biol. 1994;32:251–268. doi: 10.1007/BF00163881. [DOI] [PubMed] [Google Scholar]
  42. Sharkey KJ. Deterministic epidemiological models at the individual level. J Math Biol. 2008;57:311–331. doi: 10.1007/s00285-008-0161-7. [DOI] [PubMed] [Google Scholar]
  43. Smith GJ, et al. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature. 2009;459:1122–1125. doi: 10.1038/nature08182. [DOI] [PubMed] [Google Scholar]
  44. van Baalen M (2000) Pair approximations for different spatial geometries. In: Dieckmann U, Law R, Metz JAJ (eds) The geometry of ecological interactions: simplifying complexity. Cambridge University Press, pp 359–387
  45. Yap HP. Some topics in graph theory. In: London Mathematical Society, Lecture notes series 108. Cambridge: Cambridge University Press; 1986. [Google Scholar]

Articles from Journal of Mathematical Biology are provided here courtesy of Nature Publishing Group

RESOURCES