Abstract
This paper proposes a multi-agent system for modeling and simulation of epidemics spread management strategies. The core of the proposed approach is a generic spatial Susceptible-Infected-Recovered stochastic discrete system. Our model aims at evaluating the effect of prophylactic and mobility limitation measures on the impact and magnitude of the epidemics spread. The paper introduces the modeling approach and, next, it proceeds to the development of a multi-agent simulation system. The proposed system is implemented and evaluated using the GAMA multi-agent platform, using several simulation scenarios, while the experimental results are discussed in detail. Our model is abstract and well defined, making it very suitable as a starting point for extension and application to more detailed models of the specific problems.
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References
Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017). https://doi.org/10.1016/j.cosrev.2017.03.001
Ban, T.Q., Duong, P.L., Son, N.H., Dinh, T.V.: Covid-19 disease simulation using gama platform. In: 2020 International Conference on Computational Intelligence (ICCI), pp. 246–251 (2020). https://doi.org/10.1109/ICCI51257.2020.9247632
Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. National Acad. Sci. 99(suppl 3), 7280–7287 (2002). https://doi.org/10.1073/pnas.082080899
Bosse, T., Duell, R., Memon, Z.A., Treur, J., van der Wal, C.N.: Agent-based modeling of emotion contagion in groups. Cogn. Comput. 7(1), 111–136 (2014). https://doi.org/10.1007/s12559-014-9277-9
Bădică, A., Bădică, C., Ganzha, M., Ivanović, M., Paprzycki, M.: Multi-agent simulation of core spatial sir models for epidemics spread in a population. In: 5th International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE 2020), IEEE (2020)
Bădică, A., Bădică, C., Ivanović, M., Dănciulescu, D.: Multi-agent modelling and simulation of graph-based predator-prey dynamic systems: a BDI approach. Expert Syst. J. Knowl. Eng. 35(5), (2018). https://doi.org/10.1111/exsy.12263
Bădică, C., Budimac, Z., Burkhard, H., Ivanović, M.: Software agents: languages, tools, platforms. Comput. Sci. Inf. Syst. 8(2), 255–298 (2011). https://doi.org/10.2298/CSIS110214013B
Cuevas, E.: An agent-based model to evaluate the covid-19 transmission risks in facilities. Comput. Biol. Med. 121, (2020). https://doi.org/10.1016/j.compbiomed.2020.103827
Dang, Q.A., Hoang, M.T., Tran, D.H.: Global dynamics of a computer virus propagation model with feedback controls. J. Comput. Sci. Cybern. 36(4), 295–304 (2020). https://doi.org/10.15625/1813-9663/36/4/15555
Gaudou, B., et al.: Comokit: a modeling kit to understand, analyze, and compare the impacts of mitigation policies against the covid-19 epidemic at the scale of a city. Front. Public Health 8, 587 (2020). https://doi.org/10.3389/fpubh.2020.563247
Glinton, R., Scerri, P., Sycara, K.: Exploiting scale invariant dynamics for efficient information propagation in large teams. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, pp. 21–30. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2010), https://doi.org/10.5555/1838206.1838210
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). https://doi.org/10.1007/11553762_21
Kampik, T., Nieves, J.C.: JS-son - a lean, extensible JavaScript agent programming library. In: Dennis, L.A., Bordini, R.H., Lespérance, Y. (eds.) EMAS 2019. LNCS (LNAI), vol. 12058, pp. 215–234. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51417-4_11
Karia, R., Gupta, I., Khandait, H., Yadav, A., Yadav, A.: COVID-19 and its modes of transmission. SN Compr. Clin. Med. 2(10), 1798–1801 (2020). https://doi.org/10.1007/s42399-020-00498-4
Kazil, J., Masad, D., Crooks, A.: Utilizing python for agent-based modeling: the mesa framework. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) SBP-BRiMS 2020. LNCS, vol. 12268, pp. 308–317. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61255-9_30
Kermack, W.O., McKendrick, A.G., Walker, G.T.: A contribution to the mathematical theory of epidemics. In: Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, vol. 115 no. 772, pp. 700–721 (1927). https://doi.org/10.1098/rspa.1927.0118
Kravari, K., Bassiliades, N.: A survey of agent platforms. J. Artif. Soc. Soc. Simul. 18(1), 11 (2015). https://doi.org/10.18564/jasss.2661
Pal, C.V., Leon, F., Paprzycki, M., Ganzha, M.: A review of platforms for the development of agent systems. arXiv.org (2020). https://arxiv.org/abs/2007.08961
Spreeuwenberg, P., Kroneman, M., Paget, J.: Reassessing the Global Mortality Burden of the 1918 Influenza Pandemic. Am. J. Epidemiol. 187(12), 2561–2567 (2018). https://doi.org/10.1093/aje/kwy191
Taillandier, P., et al.: Building, composing and experimenting complex spatial models with the GAMA platform. Geo Informatica 23(2), 299–322 (2018). https://doi.org/10.1007/s10707-018-00339-6
Tolles, J., Luong, T.: Modeling epidemics with compartmental models. JAMA Guide Stat. Methods 323(24), 2515–2516 (2020). https://doi.org/10.1001/jama.2020.8420
te Vrugt, M., Bickmann, J., Wittkowski, R.: Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory. Nature Commun. 11(1), 5576 (2020). https://doi.org/10.1038/s41467-020-19024-0
Acknowledgement
This work has been supported by the joint research project “Novel methods for development of distributed systems” under the agreement on scientific cooperation between the Polish Academy of Sciences and Romanian Academy for years 2019–2021.
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Bădică, A., Bădică, C., Ganzha, M., Ivanović, M., Paprzycki, M. (2021). Multi-agent Spatial SIR-Based Modeling and Simulation of Infection Spread Management. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_37
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