1. Proceedings
  2. I3M
  3. 2022
  4. EMSS
  5. 10.46354/i3m.2022.emss.008

Parallelization of large-scale agent-based epidemiological simulations

  • a Steffen Fürst ,
  • b Christian Rakow 
  • a,b  Zuse Institut Berlin, Takustraße 7, Berlin, 14195, Germany
  • a,b  Department of Transport Systems Planning and Telematics, Technische Universität Berlin, Salzufer 17-19, Berlin, 10587, Germany
Cite as
Fürst S., and Rakow C. (2022).,Parallelization of large-scale agent-based epidemiological simulations. Proceedings of the 34th European Modeling & Simulation Symposium (EMSS 2022). , 008 . DOI: https://doi.org/10.46354/i3m.2022.emss.008

Abstract

Agent-based epidemiological models have been applied widely successfully during the SARS-CoV-2 pandemic and assisted policymakers in assessing the effectiveness of intervention strategies. The computational complexity of agent-based models is still challenging, and therefore it is important to utilize modern multi-core systems as good as possible.
In this paper, we are presenting our work on parallelizing the epidemiological simulation model MATSim Episim. Episim combines a large-scale person-centric human mobility model with a mechanistic model of infection and a person-centric disease progression model.
In general, the parallelization of agent-based models with an inherent sequential structure — in the case of epidemiological models, the temporal order of the individual movements of the agents — is challenging. Especially when the underlying social network is irregular and dynamic, they require frequent communication between the processing elements. In Episim, however, we were able to take advantage of the fact that people are not contagious on the same day they become infected, and therefore immediate health synchronization is not required. By parallelizing some of the most computationally intensive submodels, we are now able to run MATSim Episim simulations up to eight times faster than the serial version. This makes it feasible to increase the number of agents, e.g. to run simulations for the whole of Germany instead of just Berlin as before.

References

  1. Axhausen, K. W., Nagel, K., and Horni, A. (2016). TheMultiAgent Transport Simulation MATSim. Ubiquity Press.
  2. Barrett, C. L., Bisset, K. R., Eubank, S., Feng, X., and Marathe, M. V. (2008). Episimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks. 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–12.
  3. Bhatele, A., Yeom, J.-S., Jain, N., Kuhlman, C. J., Livnat, Y., Bisset, K. R., Kale, L. V., and Marathe, M. V. (2017).
    Massively parallel simulations of spread of infectious diseases over realistic social networks. In 2017 17th
    IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pages 689–694
  4. Bitencourt, J. (2021). Analyzing the impact of vaccination on covid-19 spread and hospitalizations: A multiparadigm simulationmodeling approach. In Proceedings of the 33rd European Modeling & Simulation Symposium, page nil.
  5. Grefenstette, J. J., Brown, S. T., Rosenfeld, R., DePasse, J., Stone, N. T., Cooley, P. C., Wheaton, W. D., Fyshe, A.,
    Galloway, D. D., Sriram, A., Guclu, H., Abraham, T., and Burke, D. S. (2013). Fred (a framework for reconstructing epidemic dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations. BMC PublicHealth, 13(1):940.
  6. Hinch, R., Probert, W. J., Nurtay, A., Kendall, M., Wymant, C., Hall, M., Lythgoe, K., Bulas Cruz, A., Zhao, L., Stewart, A., Ferretti, L., Montero, D., Warren, J., Mather, N., Abueg, M., Wu, N., Legat, O., Bentley, K., Mead, T.,
    Van-Vuuren, K., Feldner-Busztin, D., Ristori, T., Finkelstein, A., Bonsall, D. G., Abeler-Dörner, L., and Fraser, C.
    (2021). OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19
    including contact tracing. PLoS Computational Biology, 17(7):e1009146.
  7. Hou, C., Chen, J., Zhou, Y., Hua, L., Yuan, J., He, S., Guo, Y., Zhang, S., Jia, Q., Zhao, C., Zhang, J., Xu, G., and Jia,
    E. (2020). The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19):
    A well-mixed SEIR model analysis. Journal of Medical Virology, 92(7):841–848
  8. Kermack, W. . and Mckendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings
    of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772):700–721
  9. Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R. G., Rosenfeld, K., Hart, G. R., Núñez, R. C., Cohen, J. A.,
    Selvaraj, P., Hagedorn, B., George, L., Jastrzębski, M., Izzo, A. S., Fowler, G., Palmer, A., Delport, D., Scott,
    N., Kelly, S. L., Bennette, C. S., Wagner, B. G., Chang, S. T., Oron, A. P., Wenger, E. A., Panovska-Griffiths, J.,
    Famulare, M., and Klein, D. J. (2021). Covasim: An agentbased model of COVID-19 dynamics and interventions.
    PLoS Computational Biology, 17(7):e1009149.
  10. Leontitsis, A., Senok, A., Alsheikh-Ali, A., Nasser, Y. A., Loney, T., and Alshamsi, A. (2021). SEAHIR: A Specialized Compartmental Model for COVID-19. International Journal of Environmental Research and Public Health, 18(5):1–11.
  11. Mahdizadeh Gharakhanlou, N. and Hooshangi, N. (2020). Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran). Informatics in
    Medicine Unlocked, 20:100403.
  12. Müller, S. A., Balmer, M., Charlton, W., Ewert, R., Neumann, A., Rakow, C., Schlenther, T., and Nagel, K.
    (2021). Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based
    modelling, agent-based simulation, and mobile phone
    data. PLOS ONE, 16(10):1–32.
  13. Müller, S. A., Charlton, W., Conrad, N. D., Ewert, R., Jefferies, D., Rakow, C., Wulkow, H., Conrad, T., Schütte,
    C., and Nagel, K. (2021a). Modus-covid bericht vom 09.04.2021. Technical report, Technische Universität
    Berlin.
  14. Müller, S. A., Charlton, W., Conrad, N. D., Ewert, R., Jefferies, D., Rakow, C., Wulkow, H., Conrad, T., Schütte,
    C., and Nagel, K. (2021b). Modus-covid bericht vom 19.03.2021. Technical report, Technische Universität
    Berlin.
  15. Nagel, K., Rakow, C., and Müller, S. A. (2021). Realistic agent-based simulation of infection dynamics and percolation. Physica A: Statistical Mechanics and its Applications, 584:126322.
  16. Ndaïrou, F., Area, I., Nieto, J. J., and Torres, D. F. (2020). Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons and Fractals, 135:109846.
  17. Tolles, J. and Luong, T. (2020). Modeling Epidemics with Compartmental Models.