1. Proceedings
  2. I3M
  3. 2021
  4. DHSS
  5. 10.46354/i3m.2021.dhss.010

AI & Interoperable Simulation for Pandemics and Crisis Management

  • a Agostino G. Bruzzone , 
  • b Bharath Gadupuri, 
  • c Wolfhard Schmidt, 
  • d Orlin Nikolov, 
  • e Marina Massei, 
  • f Paolo di Bella, 
  • g Massimo Pedemonte
  • a,b,f,g  Simulation Team, 16145, Genova, Italy
  • c Ewwol Solutions Ltd, Ulica Marii Konopnickiej 47, 86-032 Niemcz, Poland (d)NATO CMDR COE, 34A Totleben Blvd, 1606 Sofia, Bulgaria.
  • e Simulation Team, SIM4Future, via Trento 43, 16145 Genova, Italy
Cite as
Bruzzone A.G., Gadupuri B., Schmidt W., Nikolov O., Massei M., di Bella P., Pedemonte M. (2021). AI & Interoperable Simulation for Pandemics and Crisis Management. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 70-76. DOI: https://doi.org/10.46354/i3m.2021.dhss.010
 Download PDF

Abstract

The adoption of Strategic Engineering as combined use of Artificial Intelligence, Simulation and Data Analytics to support decision making has a great potential in dealing with Pandemics due to their complexity and the impact of VUCA (Volatility, Uncertainty, Complexity & Ambiguity) on them. From this point of view using advanced paradigms such as MS2G and adopting interoperability standards is a major enabler to guarantee their effectiveness in dealing with these problems; this paper represents an introductory work on a joint research devoted to finalize experimentation and test to develop this new capability within Research Centers for supporting the Society during pandemics

References

  1. Allen, L.J.S., Bolker, B.M., Lou, Y., Nevai, A.L., 2008. Asymptotic profiles of the steady states for an SIS epidemic reaction-diffusion model, in: Discrete and Continuous Dynamical Systems. pp. 1–20. https://doi.org/10.3934/dcds.2008.21.1
  2. Araz, O.M., Jehn, M., Lant, T., Fowler, J.W., 2012. A new method of exercising pandemic preparedness through an interactive simulation and visualization. J. Med. Syst. 36, 1475–1483. https://doi.org/10.1007/s10916-010-9608-7
  3. Avalle L, A.G. Bruzzone, F. Copello, A. Guerci, P.Bartoletti (1999) "Epidemic Diffusion Simulation Relative to Movements of a Population that Acts on the Territory: Bio-Dynamic Comments and Evaluations", Proceedings of WMC99, San Francisco, January
  4. Avalle L., Bruzzone A.G., Copello F., Guerci A. & A.Scavotti (1995) "Preliminary Analysis for the Creation of a Territorial Epidemic Simulation Model", Proc. of ESM95, Praha, June 5-7
  5. Azevedo, J. P., Hasan, A., Goldemberg, D., Iqbal, S. A., & Geven, K. (2020). Simulating the potential impacts of covid-19 school closures on schooling and learning outcomes: A set of global estimates.
  6. Bayham, J., & Fenichel, E. P. (2020). Impact of school closures for COVID-19 on the US health-care workforce and net mortality: a modelling study. The Lancet Public Health.
  7. Bijl, J.L., Boer, C.A., 2011. Advanced 3D visualization for simulation using game technology, in: Proceedings - Winter Simulation Conference. Winter Simulation Conference, pp. 2810–2821. https://doi.org/10.1109/WSC.2011.6147985
  8. Bossomaier, T., Bruzzone, A.G., Massei, M., Newth, D., Rosen, J., 2009. Pandemic dynamic objects & reactive agents, in: International Workshop on Modeling and Applied Simulation, MAS 2009, Held at the International Mediterranean and Latin American Modeling Multiconference, I3M 2009. pp. 115–122.
  9. Backfrieder, W., Bruzzone, A., Longo, F., Novak, V., & Rosen, J. (2012) “The 1st International Workshop on Innovative Simulation for Health Care”, Vienna, Austria,
  10. Bruzzone, A.G., Sinelshchikov, K., Massei, M., 2020a. Epidemic simulation based on intelligent agents. 9th Int. Work. Innov. Simul. Heal. Care, IWISH 2020 86–91. https://doi.org/10.46354/i3m.2020.iwish.015
  11. Bruzzone, A.G., Sinelshchikov, K., Massei, M., Pedemonte, M., 2020b. Town protection simulation. 19th Int. Conf. Model. Appl. Simulation, MAS 2020 160–165. https://doi.org/10.46354/i3m.2020.mas.021
  12. Bruzzone, A.G., Sinelshchikov, K. & Massei, M. (2019). Application of blockchain in interoperable simulation for strategic decision making. International Summer Computer Simulation Conference, Summersim, SCS, Berlin, Germany, July 22-24
  13. Bruzzone, A.G., 2018a. MS2G as pillar for developing strategic engineering as a new discipline for complex problem solving, in: 30th European Modeling and Simulation Symposium, Keynote Paper at EMSS Budapest, . pp. 405–411.
  14. Bruzzone, A. G., & Di Bella, P. (2018b). Tempus Fugit: Time as the Main Parameter for the Strategic Engineering of MOOTW. Proceedings of WAMS
  15. Bruzzone, A.G., Sinelshchikov, K. & Di Matteo, R. (2018c). Population Behavior, Social Networks, Transportations, infrastructures & Urban Simulation for Decision Makers. 30th European Modeling and Simulation Symposium, EMSS, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 
  16. Bruzzone A.G., (2017a) "Smart Simulation: IA, Simulation and SG as enablers for Creating New Solutions in Engineering, Industry and Service of the Society”, Keynote Speech at International Top-level Forum on Engineering Science & Technology Development Strategy- AI and Simulation, Hangzhou, China
  17. Bruzzone A.G., Massei M., Longo F., Maglione G.L., Di Matteo R., Di Bella P., Milano V. (2017b) "Verification and Validation Applied To an Interoperable Simulation for Strategic Decision Making Involving Human Factors", Proc.of WAMS, Florence, September
  18. Bruzzone, A.G., Agresta, M. & Sinelshchikov, K. (2017c). Simulation as decision support system for disaster prevention. In proceedings of SESDE 2017, Barcelona, September
  19. Bruzzone, A. G., Massei, M., Longo, F., Cayirci, E., di Bella, P., Maglione, G. L., & Di Matteo, R. (2016). Simulation models for hybrid warfare and population simulation. In Proc. of NATO Symposium on Ready for the Predictable, Prepared for the Unexpected, M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts, Bucharest, Romania, October (pp. 17-21).
  20. Bruzzone A.G., Massei M., Agresta M., Poggi S., Camponeschi F. & M. (2014a) "Addressing Strategic Challenges on Mega Cities through MS2G", Proc. of MAS2014, Bordeaux, September
  21. Bruzzone, A., Massei, M., Longo, F., Poggi, S., Agresta, M., Bartolucci, C., & Nicoletti, L. (2014b). Human Behavior Simulation for complex scenarios based on intelligent agents. In Proceedings of the 2014 Annual Simulation Symposium (pp. 1-10), April
  22. Bruzzone A.G. (2013) “Intelligent Agent-based Simulation for supporting Operational Planning in Country Reconstruction”, Int.Journal of Simulation & Process Modelling, 8(2-3), 145-159.
  23. Bruzzone, A.G., Massei, M., Madeo, F., Tarone, F., Petuhova, J., 2011. Intelligent agents for pandemic modeling, in: Emerging M and S Applications in Industry and Academia Symposium 2011, EAIA 2011 - 2011 Spring Simulation Multiconference. pp. 23–30.
  24. Chen, L., & Carley, K. (2003). The impact of network topology on the spread of anti-virus countermeasures. In Proceedings of NAACSOS Conference.
  25. Chunara, R., Andrews, J. R., & Brownstein, J. S. (2012). Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. The American journal of tropical medicine and hygiene, 86(1), 39-45.
  26. De Rooij, D., Belfroid, E., Hadjichristodoulou, C., Mouchtouri, V.A., Raab, J., Timen, A., 2020. Educating, training, and exercising for infectious disease control with emphasis on cross-border settings: An integrative review. Global. Health 16. https://doi.org/10.1186/S12992-020-00604-0
  27. Deuffic, S., Buffat, L., Poynard, T., & Valleron, A. J. (1999). Modeling the hepatitis C virus epidemic in France. Hepatology, 29(5), 1596-1601.
  28. Di Bella P. (2015) “Present and Futures Scenarios and Challenges for M&S terms of Human Behaviour Modeling”, Invited Speech at I3M2015, Bergeggi, Italy, September 
  29. Dieckmann, P., Torgeirsen, K., Qvindesland, S.A., Thomas, L., Bushell, V., Langli Ersdal, H., 2020. The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK. Adv. Simul. 5. https://doi.org/10.1186/s41077-020-00121-5
  30. Finke, E. J., Loscher, J., & Koch, H. (1999). Planning of medical support for a threatened or actual biological environment. Principles, policies and procedures. In NBC risks current capabilities and future perspectives for protection (pp. 69-93). Springer, Dordrecht.
  31. Galvão, T.A.B., Neto, F.M.M., Bonates, M.F., Campos, M.T., 2012. A serious game for supporting training in risk management through project-based learning. Commun. Comput. Inf. Sci. 248 CCIS, 52–61. https://doi.org/10.1007/978-3-642-31800-9_6
  32. Johansen, B., & Euchner, J. (2013). Navigating the VUCA world. Research-Technology Management, 56(1), 10-15.
  33. Kaplan, E. H. (1989). What are the risks of risky sex? Modeling the AIDS epidemic. Operations Research, 37(2), 198-209.
  34. Mao, L., Bian, L., 2011. Agent-based simulation for a dual-diffusion process of influenza and human preventive behavior. Int. J. Geogr. Inf. Sci. 25, 1371–1388. https://doi.org/10.1080/13658816.2011.556121
  35. Massei M., Poggi, S., Agresta, M., & Ferrando, A. (2014) "Development Planning Based on Interoperable Agent Driven Simulation", Journal of Computational Science, 5(3), 395-407 
  36. Melnick, H., & Darling-Hammond, L. (2020). Reopening Schools in the Context of COVID-19: Health and Safety Guidelines from Other Countries. Policy Brief. Learning Policy Institute.
  37. Mossel, A., Peer, A., Goellner, J., Kaufmann, H., 2015. Requirements Analysis on a Virtual Reality Training System for CBRN Crisis Preparedness, in: Proceedings of the 59th Annual Meeting of the International Society for the Systems Sciences (ISSS). pp. 1–20.
  38. Oxman, G. L., Smolkowski, K., & Noell, J. (1996). Mathematical modeling of epidemic syphilis transmission, 23(1), 30-39.
  39. Perrotta, C., Featherstone, G., Aston, H., Houghton, E., 2013. Game-based learning: Latest evidence and future directions, NFER (National Foundation for Educational Research).
  40. Raybourn, E.M., 2014. A new paradigm for serious games: Transmedia learning for more effective training and education. J. Comput. Sci. 5, 471–481. https://doi.org/10.1016/j.jocs.2013.08.005
  41. Roda, W. C., Varughese, M. B., Han, D., & Li, M. Y. (2020). Why is it difficult to accurately predict the COVID-19 epidemic?. Infectious Disease Modelling.
  42. Stage, H. B., Shingleton, J., Ghosh, S., Scarabel, F., Pellis, L., & Finnie, T. (2020). Shut and re-open: the role of schools in the spread of COVID-19 in Europe. arXiv preprint arXiv:2006.14158.
  43. Stolar, A., 2012. Live CBRN agent training for responders as a key role in a safe crisis recovery, in: Barry, D.L., Coldewey, W.G., Reimer, D.W.G., Rudakov, D. V. (Eds.), NATO Science for Peace and Security Series - E: Human and Societal Dynamics. IOS Press, pp. 58–66. https://doi.org/10.3233/978-1-61499-039-0-58
  44.  Wentzel, K. R., & Battle, A. A. (2001). Social relationships and school adjustment. Adolescence and education: General issues in the education of adolescents, 1, 99-118.