Advertisement

Coupling Micro and Macro Dynamics Models on Networks: Application to Disease Spread

  • Arnaud Banos
  • Nathalie Corson
  • Benoit GaudouEmail author
  • Vincent Laperrière
  • Sébastien Rey Coyrehourcq
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9568)

Abstract

A hybrid model coupling an aggregated equation-based model and an agent-based model is presented in this article. It is applied to the simulation of a disease spread in a city network. We focus here on the evaluation of our hybrid model by comparing it with a simple aggregated model. We progressively introduce heterogeneities in the model and measure their impact on three indicators: the maximum intensity of the epidemic, its duration and the time of the epidemic peak. Finally we present how to integrate mitigation strategies in the model and the benefits we can get from our hybrid approach over single paradigm models.

Keywords

Hybrid model ODE Metapopulation Network Disease spread 

Notes

Acknowledgements

This work has been partially founded by the CNRS through the PEPS HuMaIn (2013 and 2014) and by the French National Network of Complex Systems (RNSC) through the interdisciplinary network MAPS (http://maps.csregistry.org/).

References

  1. 1.
    Arino, J., Van den Driessche, P.: A multi-city epidemic model. Math. Popul. Stud. 10(3), 175–193 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Cecconi, F., Campenni, M., Andrighetto, G., Conte, R.: What do agent-based and equation-based modelling tell us about social conventions: the clash between abm and ebm in a congestion game framework. J. Artif. Soc. Soc. Simul. 13(1), 6 (2010)Google Scholar
  3. 3.
    Colizza, V., Vespignani, A.: Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J. Theor. Biol. 251, 450–467 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Durrett, R., Levin, S.: The importance of being discrete (and spatial). Theor. Popul. Biol. 46(32), 363–394 (1994)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fahse, L., Wissel, C., Grimm, V.: Reconciling classical and IB approaches in theoretical population ecology: a protocol for extracting population parameters from IBM. Am. Nat. 152, 838–852 (1998)CrossRefGoogle Scholar
  6. 6.
    Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
  7. 7.
    Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F.: The ODD protocol: a review and first update. Ecol. Model. 221(23), 2760–2768 (2010)CrossRefGoogle Scholar
  8. 8.
    Hanski, I.A., Gilpin, M.E. (eds.): Metapopulation Biology: Ecology, Genetics, and Evolution. Academic Press, Waltham (1997)zbMATHGoogle Scholar
  9. 9.
    Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. J. Hyg. 39(3), 271–288 (1939)CrossRefzbMATHGoogle Scholar
  10. 10.
    Laperrière, V., Badariotti, D., Banos, A., Müller, J.-P.: Structural validation of an individual-based model for plague epidemics simulation. Ecol. Complex. 6(2), 102–112 (2009)CrossRefGoogle Scholar
  11. 11.
    Meloni, S., Perra, N., Arenas, A., Gomez, S., Moreno, Y., Vespignani, A.: Modeling human mobility responses to the large-scale spreading of infectious diseases. Sci. Rep. 1, 62(7), 1–7 (2011)Google Scholar
  12. 12.
    Van Dyke Parunak, H., Savit, R., Riolo, R.L.: Agent-based modeling vs. equation-based modeling: a case study and users’ guide. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) MABS 1998. LNCS (LNAI), vol. 1534, pp. 10–25. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Press, W.H., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes. The Art of Scientific Computing, 3rd edn. Cambridge University Press, Cambridge (2007)zbMATHGoogle Scholar
  14. 14.
    Simoes, J.: An Agent-Based Approach to Spatial Epidemics through GIS. Ph.D. thesis, University College London (2006)Google Scholar
  15. 15.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  16. 16.
    Wilensky, U., Evanston, I.: Netlogo. Center for connected learning and computer based modeling. Technical report, Northwestern University (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Arnaud Banos
    • 1
  • Nathalie Corson
    • 2
  • Benoit Gaudou
    • 3
    Email author
  • Vincent Laperrière
    • 4
  • Sébastien Rey Coyrehourcq
    • 5
  1. 1.UMR Géographie-cités, CNRS, University of Paris 1 Panthéon Sorbonne, University of Paris 7 Paris DiderotParisFrance
  2. 2.LMAH, University Le Havre, Normandie UniversityLe HavreFrance
  3. 3.UMR 5505 IRIT, CNRS, University of ToulouseToulouseFrance
  4. 4.UMR ESPACE, CNRS, University Nice Sophia Antipolis, Avignon University, Aix Marseille UniversityNiceFrance
  5. 5.UMR IDEES, CNRS, University of RouenRouenFrance

Personalised recommendations