Multi-agent Systems for Epidemiology: Example of an Agent-Based Simulation Platform for Schistosomiasis

  • Papa Alioune CisseEmail author
  • Jean Marie Dembele
  • Moussa Lo
  • Christophe Cambier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10685)


In this paper, we show the convenience of multi-agent systems to help computational epidemiology come to the rescue of mathematical epidemiology for its practical limits on modeling and simulation of complex epidemiological phenomena. Herein, we propose as an example, an agent-based simulation platform for schistosomiasis (commonly known as Bilharzia, which is a parasitic disease found in tropical and subtropical areas and caused by a tapeworm called schistosome or bilharzias) that we have experimented with actual data of schistosomiasis in Niamey (Niger).


Complex systems Mathematical modeling Multi-agents system Agent-based modeling and simulation Schistosomiasis Computational epidemiology 


  1. 1.
    Patlolla, P., Gunupudi, V., Mikler, A.R., Jacob, R.T.: Agent-based simulation tools in computational epidemiology. In: Böhme, T., Larios Rosillo, V.M., Unger, H., Unger, H. (eds.) IICS 2004. LNCS, vol. 3473, pp. 212–223. Springer, Heidelberg (2006). CrossRefGoogle Scholar
  2. 2.
    Bonabeau, E., Toubiana, L., Flahault, A.: Evidence for global mixing in real influenza epidemics. J. Phys. Math. Gen. 31(19), L361 (1998)CrossRefGoogle Scholar
  3. 3.
    Marathe, M., Ramakrishnan, N.: Recent advances in computational epidemiology. IEEE Intell. Syst. 28(4), 96–101 (2013)CrossRefGoogle Scholar
  4. 4.
    Fuks, H., Lawniczak, A.T.: Individual-based lattice model for spatial spread of epidemics. Discrete Dyn. Nat. Soc. 6(3), 191–200 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fukś, H., Duchesne, R., Lawniczak, A.T.: Spatial correlations in SIR epidemic models, mai 2005. arXiv:nlin/0505044
  6. 6.
    Murray, J.D., Stanley, E.A., Brown, D.L.: On the spatial spread of rabies among foxes. Proc. R. Soc. Lond. B Biol. Sci. 229(1255), 111–150 (1986)CrossRefGoogle Scholar
  7. 7.
    Fu, S.C., Milne, G.: A flexible automata model for disease simulation. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 642–649. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  8. 8.
    Barrett, C.L., Eubank, S., Marathe, M.V.: An interaction-based approach to computational epidemiology. In: AAAI, pp. 1590–1593 (2008)Google Scholar
  9. 9.
    Gorder, P.F.: Computational epidemiology. Comput. Sci. Eng. 12(1), 4–6 (2010)CrossRefGoogle Scholar
  10. 10.
    Perez, L., Dragicevic, S.: An agent-based approach for modeling dynamics of contagious disease spread. Int. J. Health Geogr. 8, 50 (2009)CrossRefGoogle Scholar
  11. 11.
    O’Hare, A., Lycett, S.J., Doherty, T., Salvador, L.C.M., Kao, R.R.: Broadwick: a framework for computational epidemiology. BMC Bioinform. 17, 65 (2016)CrossRefGoogle Scholar
  12. 12.
    Siebert, J.: Approche multi-agent pour la multi-modélisation et le couplage de simulations. Application à l’étude des influences entre le fonctionnement des réseaux ambiants et le comportement de leurs utilisateurs. Ph.D. thesis, Université Henri Poincaré - Nancy I (2011)Google Scholar
  13. 13.
    Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agents Multi-agent Syst. 14(1), 5–30 (2007)CrossRefGoogle Scholar
  14. 14.
    Stratulat, T., Ferber, J., Tranier, J.: MASQ: towards an integral approach to interaction. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 813–820 (2009)Google Scholar
  15. 15.
    Ferber, J.: Les systèmes multi-agents: un aperçu général. Tech. Sci. Inform. 16(8) (1997)Google Scholar
  16. 16.
    Demange, J.: Un modèle d’environnement pour la simulation multiniveau - Application à la simulation de foules. Ph.D. thesis, Université de Technologie de Belfort-Montbeliard (2012)Google Scholar
  17. 17.
    Muller, J.P.: Des systemes autonomes aux systemes multi-agents: Interaction, emergence et systemes complexes, HDR, Habilitation a diriger des recherches – Informatique (2002)Google Scholar
  18. 18.
    Jennings, N.R., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Auton. Agents Multi-Agent Syst. 1(1), 7–38 (1998)CrossRefGoogle Scholar
  19. 19.
    Chaib-Draa, B., Jarras, I., Moulin, B.: Systèmes multi-agents: principes généraux et applications. Ed. Hermès, pp. 1030–1044 (2001)Google Scholar
  20. 20.
    Payet, D., Courdier, R., Sebastien, N., Ralambondrainy, T.: Environment as support for simplification, reuse and integration of processes in spatial MAS. In: 2006 IEEE International Conference on Information Reuse and Integration, pp. 127–131 (2006)Google Scholar
  21. 21.
    Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A.: Multi-agent oriented programming with JaCaMo. Sci. Comput. Program. 78(6), 747–761 (2013)CrossRefGoogle Scholar
  22. 22.
    Treuil, J.-P., Drogoul, A., Zucker, J.-D.: Modélisation et simulation à base d’agents: exemples commentés, outils informatiques et questions théoriques. Dunod: IRD, Paris (2008)Google Scholar
  23. 23.
    Gaud, N.A.: Systèmes multi–agents holoniques: de l’analyse à l’implantation: méta-modèle, méthodologie, et simulation multi-niveaux. Besançon (2007)Google Scholar
  24. 24.
    Gil-Quijano, J., Hutzler, G., Louail, T.: De la cellule biologique à la cellule urbaine: retour sur trois expériences de modélisation multi-échelles à base d’agents. In: 17ème Journées Francophones sur les Systèmes Multi-Agents (JFSMA 2009), Lyon, France, pp. 187–198 (2009)Google Scholar
  25. 25.
    Servat, D., Perrier, E., Treuil, J.-P., Drogoul, A.: When agents emerge from agents: introducing multi-scale viewpoints in multi-agent simulations. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) MABS 1998. LNCS, vol. 1534, pp. 183–198. Springer, Heidelberg (1998). CrossRefGoogle Scholar
  26. 26.
    Maquerlot, F., et al.: Dual role for plasminogen activator inhibitor type 1 as soluble and as matricellular regulator of epithelial alveolar cell wound healing. Am. J. Pathol. 169(5), 1624–1632 (2006)CrossRefGoogle Scholar
  27. 27.
    Dembele, J.M., Cambier, C.: An agent-particle model for taxis-based aggregation; emergence and detection of structures. Procedia Comput. Sci. 9, 1484–1493 (2012)CrossRefGoogle Scholar
  28. 28.
    Khalil, K.M., Abdel-Aziz, M., Nazmy, T.T., Salem, A.B.M.: An agent-based modeling for pandemic influenza in Egypt. In: Lu, J., Jain, L.C., Zhang, G. (eds.) Handbook on Decision Making. Intelligent Systems Reference Library, vol. 33, pp. 205–218. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  29. 29.
    Shi, Z.Z., Wu, C.-H., Ben-Arieh, D.: Agent-based model: a surging tool to simulate infectious diseases in the immune system. Open J. Model. Simul. 2(1), 12–22 (2014)CrossRefGoogle Scholar
  30. 30.
    Reyes, A.M., Diaz, H., Olarte, A.: An agent-based model for the control of malaria using genetically modified vectors. In: ECMS, pp. 31–36 (2012)Google Scholar
  31. 31.
    Ferrer, J., Albuquerque, J., Prats, C., López, D., Valls, J.: Agent-based models in malaria elimination strategy design. In: EMCSR 2012 (2012)Google Scholar
  32. 32.
    Daudé, É., Vaguet, A., Paul, R.: La dengue, maladie complexe. Nat. Sci. Sociétés 23(4), 331–342 (2015)CrossRefGoogle Scholar
  33. 33.
    Paul, P.N.T., Bah, A., Ndiaye, P.I., Ndione, J.A.: An agent-based model for studying the impact of herd mobility on the spread of vector-borne diseases: the case of rift valley fever (Ferlo Senegal). Open J. Model. Simul. 2(3), 97–111 (2014)CrossRefGoogle Scholar
  34. 34.
    Kloos, H., Gazzinelli, A., Van Zuyle, P.: Microgeographical patterns of schistosomiasis and water contact behavior; examples from Africa and Brazil. Mem. Inst. Oswaldo Cruz 93(Suppl 1), 37–50 (1998)CrossRefGoogle Scholar
  35. 35.
    Watts, S., Khallaayoune, K., Bensefia, R., Laamrani, H., Gryseels, B.: The study of human behavior and schistosomiasis transmission in an irrigated area in Morocco. Soc. Sci. Med. 1982 46(6), 755–765 (1998)Google Scholar
  36. 36.
    Barbosa, C.S.: Epidemiology and anthropology: an integrated approach dealing with bio-socio-cultural aspects as strategy for the control of endemic diseases. Mem. Inst. Oswaldo Cruz 93(Suppl 1), 59–62 (1998)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Fianyo, Y.E.: Couplage de modèles à l’aide d’agents: le système OSIRIS, Paris 9 (2001)Google Scholar
  38. 38.
    Cisse, P.A., Dembele, J.M., Lo, M., Cambier, C.: Assessing the spatial impact on an agent-based modeling of epidemic control: case of schistosomiasis. In: Glass, K., Colbaugh, R., Ormerod, P., Tsao, J. (eds.) Complex 2012. LNICSSITE, vol. 126, pp. 58–69. Springer, Cham (2013). CrossRefGoogle Scholar
  39. 39.
    Cisse, P.A., Dembele, J.M., Cambier, C., Lo, M.: Multi-agent simulation of water contact’s patterns in relation to schistosomiasis: a BDI architecture using kernel functions. In: 2014 Second World Conference on Complex Systems (WCCS), pp. 536–541 (2014)Google Scholar
  40. 40.
    Bordini, R.H., Hübner, J.F., Wooldridge, M.: Programming Multi-agent Systems in AgentSpeak Using Jason. Wiley Series in Agent Technology. Wiley, Chichester (2007)CrossRefzbMATHGoogle Scholar
  41. 41.
    Drogoul, A., et al.: GAMA: a spatially explicit, multi-level, agent-based modeling and simulation platform. In: Demazeau, Y., Ishida, T., Corchado, J.M., Bajo, J. (eds.) PAAMS 2013. LNCS, vol. 7879, pp. 271–274. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  42. 42.
    Hägerstrand, T.: What about people in regional science? Pap. Reg. Sci. Assoc. 24(1), 6–21 (1970)CrossRefGoogle Scholar
  43. 43.
    Miller, H.J.: A measurement theory for time geography. Geogr. Anal. 37(1), 17–45 (2005)CrossRefGoogle Scholar
  44. 44.
    Ernould, J.-C., Labbo, R., Chippaux, J.-P.: Evolution de la schistosomose urinaire à Niamey. Niger. Bull. Société Pathol. Exot. 96(3), 173–177 (2003)Google Scholar
  45. 45.
    Ernould, J.C., Kaman Kaman, A., Labbo, R., Couret, D., Chippaux, J.P.: Recent urban growth and urinary schistosomiasis in Niamey, Niger. Trop. Med. Int. Health 5(6), 431–437 (2000)CrossRefGoogle Scholar
  46. 46.
    Bordini, R.H., Bazzan, A.L.C., Jannone, R. de O., Basso, D.M., Vicari, R.M., Lesser, V.R.: AgentSpeak(XL): efficient intention selection in BDI agents via decision-theoretic task scheduling. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 3, New York, pp. 1294–1302 (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Papa Alioune Cisse
    • 1
    • 2
    Email author
  • Jean Marie Dembele
    • 1
    • 2
  • Moussa Lo
    • 1
    • 2
    • 3
  • Christophe Cambier
    • 2
  1. 1.LANI, UFR SATUniversité Gaston BergerSaint-LouisSénégal
  2. 2.UMI 209 UMMISCOParisFrance
  3. 3.LIRIMA, M2EIPSSaint-LouisSénégal

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