World Scientific
  • Search
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

On the continuum limit of epidemiological models on graphs: Convergence and approximation results

    https://doi.org/10.1142/S0218202524500271Cited by:0 (Source: Crossref)

    We focus on an epidemiological model (the archetypical SIR system) defined on graphs and study the asymptotic behavior of the solutions as the number of vertices in the graph diverges. By relying on the theory of graphons we provide a characterization of the limit and establish convergence results. We also provide approximation results for both deterministic and random discretizations.

    Communicated by N. Bellomo

    AMSC: 35Q92, 92D30, 35R02, 05C99

    References

    • 1. Y. Alimohammadi, C. Borgs and A. Saberi, Algorithms using local graph features to predict epidemics, in Proc. 2022 Annual ACM-SIAM Symp. Discrete Algorithms (SODA) (2022), pp. 3430–3451. CrossrefGoogle Scholar
    • 2. L. Almeida, P.-A. Bliman, G. Nadin, B. Perthame and N. Vauchelet, Final size and convergence rate for an epidemic in heterogeneous population, Math. Models Methods Appl. Sci. 31 (2021) 1021–1051. Link, Web of ScienceGoogle Scholar
    • 3. M. Barthelemy, A. Barrat, R. Pastor–Satorras and A. Vespignani, Dynamical patterns of epidemic outbreaks in complex heterogeneous networks, J. Theor. Biol. 235 (2005) 275–288. Crossref, Web of ScienceGoogle Scholar
    • 4. N. Bellomo, R. Bingham, M. A. J. Chaplain, G. Dosi, G. Forni, D. A. Knopof, J. Lowengrub, R. Twarock and M. E. Virgillito, A muliscale model of virus pandemic: Heterogeneous interactive entities in a globally connected world, Math. Models Methods Appl. Sci. 30 (2020) 1591–1651. Link, Web of ScienceGoogle Scholar
    • 5. I. Benjamini and O. Schramm, Recurrence of distributional limits of finite planar graphs, Electr. J. Probab. 6 (2001) 1–13. Crossref, Web of ScienceGoogle Scholar
    • 6. D. Bernoulli, Essai d’une nouvelle analyse de la mortalité causée par la petite vérole et des avantages de l’inoculation pour la prévenir, Mémoires de Mathématiques et de Physique (Académie Royale des Sciences, 1760), pp. 1–45. Google Scholar
    • 7. C. Borgs, J. T. Chayes, H. Cohn and S. Ganguly, Consistent nonparametric estimation for heavy-tailed sparse graphs, Ann. Statist. 49 (2021) 1904–1930. Crossref, Web of ScienceGoogle Scholar
    • 8. C. Borgs, J. T. Chayes, H. Cohn and Y. Zhao, An Lp theory of sparse graph convergence II: LD convergence, quotients, and right convergence, Ann. Prob. 45 (2018) 337–396. Google Scholar
    • 9. C. Borgs, J. T. Chayes, H. Cohn and Y. Zhao, An Lp theory of sparse graph convergence I: Limits, sparse random graph models, and power law distributions, Trans. Am. Math. Soc. 372 (2019) 3019–3062. Crossref, Web of ScienceGoogle Scholar
    • 10. C. Borgs, J. T. Chayes and L. Lovász, Moments of two–variable functions and the uniqueness of graph limits, Geom. Funct. Anal. 19 (2010) 1597–1619. Crossref, Web of ScienceGoogle Scholar
    • 11. C. Borgs, J. T. Chayes, L. Lovász, V. T. Sós and K. Vesztergombi, Counting graph homomorphisms, Top. Discr. Math. Ser. Algor. Combin. 26 (2006) 315–371. CrossrefGoogle Scholar
    • 12. C. Borgs, J. T. Chayes, L. Lovász, V. T. Sós and K. Vesztergombi, Convergent sequences of dense graphs I. Subgraph frequencies, metric properties and testing, Adv. Math. 219 (2008) 1801–1851. Crossref, Web of ScienceGoogle Scholar
    • 13. C. Borgs, J. T. Chayes, L. Lovász, V. T. Sós and K. Vesztergombi, Convergent sequences of dense graphs II. Multiway cuts and statistical physics, Ann. Math. 176 (2012) 151–219. Crossref, Web of ScienceGoogle Scholar
    • 14. A. Braides, P. Cermelli and S. Dovetta, Γ-limit of the cut functional on dense graph sequences, ESAIM: COCV 26 (2020) 26. CrossrefGoogle Scholar
    • 15. F. Brauer and C. Castillo-Chávez, Mathematical Models in Population Biology and Epidemiology (Springer–Verlag, 2001). CrossrefGoogle Scholar
    • 16. F. Brauer, P. van den Driessche and J. Wu (eds.), Mathematical Epidemiology, Lecture Notes in Mathematics, Vol. 1945 (Springer-Verlag, 2008). CrossrefGoogle Scholar
    • 17. E. Çinlar, Probability and Stochastics, Graduate Texts in Mathematics, Vol. 261 (Springer-Verlag, New York, 2011). CrossrefGoogle Scholar
    • 18. J.-F. Delmas, D. Dronnier and P.-A. Zitt, An infinite-dimensional metapopulation SIS model, J. Diff. Eq. 313 (2022) 1–53. Crossref, Web of ScienceGoogle Scholar
    • 19. D. J. Daley and J. Gani, Epidemic Modeling: An Introduction (Cambridge University Press, 2005). Google Scholar
    • 20. P. Diaconis and D. Freedman, On the statistics of vision: The Julesz Conjecture, J. Math. Psychol. 2 (1981) 112–138. CrossrefGoogle Scholar
    • 21. S. N. Dorogovtsev, A. V. Goltsev and J. F. F. Mendes, Critical phenomena in complex networks, Rev. Mod. Phys. 80 (2008) 1275–1335. Crossref, Web of ScienceGoogle Scholar
    • 22. A. Frieze and R. Kannan, Quick approximation to matrices and applications, Combinatorica 19 (1999) 175–220. Crossref, Web of ScienceGoogle Scholar
    • 23. S. Gao and P. E. Caines, Spectral representations of graphons in very large network systems control, IEEE Conf. Decision and Control (2019), pp. 5068–5075. CrossrefGoogle Scholar
    • 24. H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev. 42 (2000) 599–653. Crossref, Web of ScienceGoogle Scholar
    • 25. S. Janson, Graphons, Cut Norm and Distance, Couplings and Rearrangements, New York, J. Math. Monogr., Vol. 4 (2013). Google Scholar
    • 26. D. Kaliuzhnyi-Verbovetskyi and G. S. Medvedev, The semilinear heat equation on sparse random graphs, SIAM J. Math. Anal. 49 (2017) 1333–1355. Crossref, Web of ScienceGoogle Scholar
    • 27. D. Kaliuzhnyi-Verbovetskyi and G. S. Medvedev, Sparse Monte Carlo method for nonlocal diffusion problems, SIAM J. Num. Anal. 60 (2022) 3001–3028. Crossref, Web of ScienceGoogle Scholar
    • 28. W. O. Kermack and A. G. McKendrick, Contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A 115 (1927) 700–721. CrossrefGoogle Scholar
    • 29. L. Lovász, Large Networks and Graph Limits, American Mathematical Society, Vol. 60 (Colloquium Publications, 2012). CrossrefGoogle Scholar
    • 30. L. Lovász and B. Szegedy, Limits of dense graph sequences, J. Combin. Theory Ser. B 96 (2006) 933–957. Crossref, Web of ScienceGoogle Scholar
    • 31. L. Lovász and B. Szegedy, Szemerédy lemma’s for analyst, Geom. Funct. Anal. 17 (2007) 252–270. Web of ScienceGoogle Scholar
    • 32. G. S. Medvedev, The nonlinear heat equation on dense graphs and graph limits, SIAM J. Math. Anal. 46 (2014) 2743–2766. Crossref, Web of ScienceGoogle Scholar
    • 33. G. S. Medvedev, The nonlinear heat equation on W-random graphs, Arch. Rational Mech. Anal. 212 (2014) 781–803. Crossref, Web of ScienceGoogle Scholar
    • 34. G. S. Medvedev, Correction to: The nonlinear heat equation on W-random graphs, Arch. Rational Mech. Anal. 231 (2019) 1305–1308. Crossref, Web of ScienceGoogle Scholar
    • 35. G. S. Medvedev, The continuum limit of the Kuramoto model on sparse random graphs, Commun. Math. Sci. 17 (2019) 883–898. Crossref, Web of ScienceGoogle Scholar
    • 36. G. S. Medvedev and X. Tang, The Kuramoto model on power law graphs: Synchronization and contrast states, J. Nonlinear Sci. 30 (2020) 2405–2427. Crossref, Web of ScienceGoogle Scholar
    • 37. A. G. McKendrick, Applications of mathematics to medical problems, Proc. Edinburgh Math. Soc. 44 (1926) 98–130. CrossrefGoogle Scholar
    • 38. Y. Moreno, R. Pastor–Satorras and A. Vespignani, Epidemic outbreaks in complex heterogeneous networks, Eur. Phys. J. B 26 (2002) 521–529. Crossref, Web of ScienceGoogle Scholar
    • 39. M. E. J. Newman, Spread of epidemic disease on networks, Phys. Rev. E 66 (2002) 016128. Crossref, Web of ScienceGoogle Scholar
    • 40. J. D. Murray, Mathematical Biology: I. An Introduction, 3rd edn., Interdisciplinary Applied Mathematics, Vol. 17 (Springer-Verlag, 2002). CrossrefGoogle Scholar
    • 41. J. D. Murray, Mathematical Biology: II. Spatial Models and Biomedical Applications, 3rd edn., Interdisciplinary Applied Mathematics, Vol. 18 (Springer–Verlag, 2003). CrossrefGoogle Scholar
    • 42. C. Nowzari, V. M. Preciado and G. J. Pappas, Analysis and control of epidemics: A survey of spreading processes on complex networks, IEEE Control Syst. Mag. 36 (2016) 26–46. Crossref, Web of ScienceGoogle Scholar
    • 43. R. Pastor–Satorras, C. Castellano, P. Van Mieghem and A. Vespignani, Epidemic processes in complex networks, Rev. Mod. Phys. 87 (2015) 925–979. Crossref, Web of ScienceGoogle Scholar
    • 44. R. Pastor-Satorras and A. Vespignani, Epidemic dynamics and endemic states in complex networks, Phys. Rev. E 63 (2001) 066117. Crossref, Web of ScienceGoogle Scholar
    • 45. R. Vizuete, P. Frasca and F. Garin, Graphon-based sensitivity analysis of SIS epidemics, IEEE Control Syst. Lett. 4 (2020) 542–547. CrossrefGoogle Scholar
    Remember to check out the Most Cited Articles!

    View our Mathematical Modelling books
    Featuring authors Frederic Y M Wan, Gregory Baker and more!