543
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

, ORCID Icon, , , &
Received 09 Mar 2022, Accepted 01 Mar 2023, Published online: 20 Mar 2023

References

  • Abduaziz, O., Cheng, J. K., Tahar, R. M., & Varma, R. (2015). A hybrid simulation model for green logistics assessment in automotive industry. Procedia Engineering, 100, 960–969. https://doi.org/10.1016/j.proeng.2015.01.455
  • Adacher, L., & Flamini, M. (2020). Optimizing airport land side operations: Check-in, passengers’ migration, and security control processes. Journal of Advanced Transportation, 2020, 6328016. https://doi.org/10.1155/2020/6328016
  • Ajelli, M., Gonçalves, B., Balcan, D., Colizza, V., Hu, H., Ramasco, J. J., Merler, S., & Vespignani, A. (2010). Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models. BMC Infectious Diseases, 10(1), 190. https://doi.org/10.1186/1471-2334-10-190
  • Alginahi, Y. M., Kabir, M. N., & Mohamed, A. I. (2013). Optimization of high-crowd-density facilities based on discrete event simulation. Malaysian Journal of Computer Science, 26, 312–329. https://ejournal.um.edu.my/index.php/MJCS/article/view/6789
  • Alodhaibi, S., Burdett, R. L., & Yarlagadda, P. K. D. V. (2017). Framework for airport outbound passenger flow modelling. Procedia Engineering, 174, 1100–1109. https://doi.org/10.1016/j.proeng.2017.01.263
  • Al-Qahtani, M., AlAli, S., AbdulRahman, A., Alsayyad, A. S., Otoom, S., & Atkin, S. L. (2021). The prevalence of asymptomatic and symptomatic COVID-19 in a cohort of quarantined subjects. International Journal of Infectious Diseases, 102, 285–288. https://doi.org/10.1016/j.ijid.2020.10.091
  • Alvanchi, A., Lee, S., & AbouRizk, S. (2011). Modeling framework and architecture of hybrid system dynamics and discrete event simulation for construction. Computer-Aided Civil and Infrastructure Engineering, 26(2), 77–91. https://doi.org/10.1111/j.1467-8667.2010.00650.x
  • Alzraiee, H., Zayed, T., & Moselhi, O. (2012). Methodology for synchronizing discrete event simulation and system dynamics models. Proceedings of the 2012 Winter Simulation Conference (WSC), 606–616. Berlin, Germany. [https://doi.org/10.1109/WSC.2012.6464997]
  • Amouroux, E., Desvaux, S., & Drogoul, A. (2008). Towards virtual epidemiology: An agent-based approach to the modeling of H5N1 propagation and persistence in North-Vietnam. In: Bui, T. D., Ho, T. V., Ha, Q. T. Eds. Intelligent Agents and Multi Eds. Intelligent Agents and MultiAgent Systems. PRIMA. Springer. Lecture Notes in Computer Science Vol. 5357. https://doi.org/10.1007/978-3-540-89674-6_6
  • Asgary, A., Najafabadi, M. M., Karsseboom, R., & Wu, J. (2020). A drive-through simulation tool for mass vaccination during COVID-19 pandemic. Healthcare (Basel), 8(4), 469. https://doi.org/10.3390/healthcare8040469
  • Barbosa, C., & Azevedo, A. (2017). Hybrid simulation for complex manufacturing value-chain environments. Procedia Manufacturing, 11, 1404–1412. https://doi.org/10.1016/j.promfg.2017.07.270
  • Bazant, M. Z., & Bush, J. W. M. (2021). A guideline to limit indoor airborne transmission of COVID-19. Proceedings of the National Academy of Sciences, 118(17), e2018995118. https://doi.org/10.1073/pnas.2018995118
  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl_3), 7280–7287. https://doi.org/10.1073/pnas.082080899
  • Brailsford, S. C. (2015). Hybrid simulation in healthcare: New concepts and new tools. Proceedings of the 2015 Winter Simulation Conference (WSC), 1645–1653. Huntington Beach, CA, USA. [https://doi.org/10.1109/WSC.2015.7408284]
  • Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2020). Hybrid simulation modelling in operational research: A state-of-the-art review. European Journal of Operational Research, 278(3), 721–737. https://doi.org/10.1016/j.ejor.2018.10.025
  • Brush, S. G. (1968). A history of random processes: I. Brownian movement from Brown to Perrin. Archive for History of Exact Sciences, 5(1), 1–36. Accessed June 20, 2021. http://www.jstor.org/stable/41133279. https://doi.org/10.1007/BF00328110.
  • Burki, T. (2020). China’s successful control of COVID-19. The Lance Infectious Diseases, 20(11), 1240–1241. https://doi.org/10.1016/S1473-3099(20)30800-8
  • Cecconi, F., Campenni, M., Andrighetto, G., & Conte, R. (2010). What do agent-based and equation-based modelling tell us about social conventions: The clash between ABM and EBM in a congestion game framework. Journal of Artificial Societies and Social Simulation, 13(1). https://doi.org/10.18564/jasss.1585
  • Cha, M. H., & Mun, D. (2014). Discrete event simulation of Maglev transport considering traffic waves. Journal of Computational Design and Engineering, 1(4), 233–242. https://doi.org/10.7315/JCDE.2014.023
  • Chen, L. (2012). Agent-based modeling in urban and architectural research: A brief literature review. Frontiers of Architectural Research, 1(2), 166–177. https://doi.org/10.1016/j.foar.2012.03.003
  • Chen, J., Lu, H., Melino, G., Boccia, S., Piacentini, M., Ricciardi, W., Wang, Y., Shi, Y., & Zhu, T. (2020). COVID-19 infection: The China and Italy perspectives. Cell Death & Diseases, 11(6), 438. https://doi.org/10.1038/s41419-020-2603-0
  • Chong, K. C., & Ying Zee, B. C. (2012). Modeling the impact of air, sea, and land travel restrictions supplemented by other interventions on the emergence of a new influenza pandemic virus. BMC Infectious Diseases, 12(1), 309. https://doi.org/10.1186/1471-2334-12-309
  • Cimini, C., Pezzotta, G., Lagorio, A., Pirola, F., & Cavalieri, S. (2021). How can hybrid simulation support organizations in assessing COVID-19 containment measures? Healthcare, 9(11), 1412. https://doi.org/10.3390/healthcare9111412
  • Currie, C. S. M., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 14(2), 83–97. https://doi.org/10.1080/17477778.2020.1751570
  • de Oliveira, M. L. M., Montevechi, J. A. B., de Pinho, A. F., & Miranda, R. D. C. (2017). Using hybrid simulation to represent the human factor in production systems. International Journal of Simulation Modelling, 16(2), 263–274. https://doi.org/10.2507/IJSIMM16(2)7.378
  • Dommar, C. J., Lowe, R., Robinson, M., & Rodó, X. (2014). An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak. Acta tropica, 129, 61–73. https://doi.org/10.1016/j.actatropica.2013.08.004
  • dos Santos, V. H., Kotiadis, K., & Scaparra, M. P. (2021). A review of hybrid simulation in healthcare. Proceedings of the 2020 Winter Simulation Conference(WSC), 1004–1015. Orlando, FL, USA. [https://doi.org/10.1109/WSC48552.2020.9383913]
  • Editorial. (2020). COVID-19 transmission—up in the air. The Lancet Respiratory Medicine, 8(12), 1159. https://doi.org/10.1016/S2213-2600(20)30514-2
  • Eldabi, T., Balaban, M., Brailsford, S., Mustafee, N., Nance, R. E., Onggo, B. S., & Sargent, R. G. (2016). Hybrid simulation: Historical lessons, present challenges and futures. Proceedings of the 2016 Winter Simulation Conference (WSC), 1388–1403. [https://doi.org/10.1109/WSC.2016.7822192]
  • Eldabi, T., Brailsford, S. C., Djanatliev, A., Kunc, M., Mustafee, N., & Osorio, A. F. (2018). Hybrid simulation challenges and opportunities: A life-cycle approach. Proceedings of the 2018 Winter Simulation Conference(WSC), 1500–1514. Gothenburg, Sweden. [https://doi.org/10.1109/WSC.2018.8632465]
  • Eldabi, T., Paul, R. J., & Young, T. (2007). Simulation modelling in healthcare: Reviewing legacies and investigating futures. The Journal of the Operational Research Society, 58(2), 262–270. https://doi.org/10.1057/palgrave.jors.2602222
  • Eldabi, T., Tako, A. A., Bell, D., & Tolk, A. (2019). Tutorial on means of hybrid simulation. Proceedings of the 2019 Winter Simulation Conference(WSC), 33–43. National Harbor, MD, USA. [https://doi.org/10.1109/WSC40007.2019.9004712]
  • Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12. https://www.jasss.org/11/4/12.html
  • Epstein, J. M. (2009). Modeling to contain pandemics. Nature, 460(7256), 687. https://doi.org/10.1038/460687a
  • Espinosa-Aranda, J. L., & García-Ródenas, R. (2012). A discrete event-based simulation model for real-time traffic management in railways. Journal of Intelligent Transportation Systems, 16(2), 94–107. https://doi.org/10.1080/15472450.2012.671713
  • Fang, L. Q., De Vlas, S. J., Feng, D., Liang, S., Xu, Y. F., Zhou, J. P., Richardus, J. H., & Cao, W. C. (2009). Geographical spread of SARS in mainland China. Tropical Medicine & International Health, 14, 14–20. https://doi.org/10.1111/j.1365-3156.2008.02189.x
  • Farina, F., Fontanelli, D., Garulli, A., Giannitrapani, A., Prattichizzo, D., & Tang, T. (2017). Walking ahead: The headed social force model. PLoS One, 12(1), e0169734. https://doi.org/10.1371/journal.pone.0169734
  • Fennelly, K. P. (2020). Particle sizes of infectious aerosols: Implications for infection control. The Lancet Respiratory Medicine, 8(9), 914–924. https://doi.org/10.1016/S2213-2600(20)30323-4
  • Gerardin, J., Ouédraogo, A. L., McCarthy, K. A., Eckhoff, P. A., & Wenger, E. A. (2015). Characterization of the infectious reservoir of malaria with an agent-based model calibrated to age-stratified parasite densities and infectiousness. Malaria journal, 14(1), 231. https://doi.org/10.1186/s12936-015-0751-y
  • Greasley, A. (2005). Using system dynamics in a discrete-event simulation study of a manufacturing plant. International Journal of Operations & Production Management, 25(6), 534–548. https://doi.org/10.1108/01443570510599700
  • Greenhalgh, T., Jimenez, J. L., Prather, K. A., Tufekci, Z., Fisman, D., & Schooley, R. (2021). Ten Scientific reasons in support of airborne transmission of SARS-CoV-2. The Lancet, 397(10285), 1603–1605. https://doi.org/10.1016/S0140-6736(21)00869-2
  • Gunawan, F. E. (2014). Design and implementation of discrete-event simulation framework for modeling bus rapid transit system. Journal of Transportation Systems Engineering and Information Technolgoy, 14(4), 37–45. https://doi.org/10.1016/S1570-6672(13)60139-7
  • Guo, X., Tong, J., Chen, P., Fan, W., & Cherifi, H. (2021). The suppression effect of emotional contagion in the COVID-19 pandemic: A multi-layer hybrid modelling and simulation approach. PLoS One, 16(7), e0253579. https://doi.org/10.1371/journal.pone.0253579
  • Hao, Q., & Shen, W. (2008). Implementing a hybrid simulation model for a Kanban-based material handling system. Robotics Computer-Integrated Manufacturing, 24(5), 635–646. https://doi.org/10.1016/j.rcim.2007.09.012
  • Hassannayebi, E., Memarpour, M., Mardani, S., Shakibayifar, M., Bakhshayeshi, I., & Espahbod, S. (2020). A hybrid simulation model of passenger emergency evacuation under disruption scenarios: A case study of a large transfer railway station. Journal of Simulation, 14(3), 204–228. https://doi.org/10.1080/17477778.2019.1664267
  • Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3), 1–33. https://www.jasss.org/5/3/2.html
  • Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–4286. https://doi.org/10.1103/PhysRevE.51.4282
  • Hoertel, N., Blachier, M., Blanco, C., Olfson, M., Massetti, M., Rico, M. S., Limosin, F., & Leleu, H. (2020). A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nature Medicine, 26(9), 1417–1421. https://doi.org/10.1038/s41591-020-1001-6
  • Hou, C., Chen, J., Zhou, Y., Hua, L., Yuan, J., He, S., Guo, Y., Zhang, S., Jia, Q., Zhao, C., Zhang, J., Xu, G., & 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. https://doi.org/10.1002/jmv.25827
  • Hsieh, Y. -H., Lee, J. -Y., & Chang, H. -L. (2004). SARS epidemiology modeling. Emerging Infectious Diseases, 10(6), 1165–1167. https://doi.org/10.3201/eid1006.031023
  • Hu, M., Lin, H., Wang, J., Xu, C., Tatem, A. J., Meng, B., Zhang, X., Liu, Y., Wang, P., Wu, G., Xie, H., & Lai, S. (2021). Risk of coronavirus disease 2019 transmission in train passengers: An epidemiological and modeling study. Clinical Infectious Diseases, 72(4), 604–610. https://doi.org/10.1093/cid/ciaa1057
  • Hunter, E., Namee, B. M., & Kelleher, J. (2019). An open-data-driven agent-based model to simulate infectious disease outbreaks. PLoS One, 14(1), e0211245. https://doi.org/10.1371/journal.pone.0211245
  • Jackson, M. L. (2020). Low-impact social distancing interventions to mitigate local epidemics of SARS-CoV-2. Microbes and Infection, 22(10), 611–616. https://doi.org/10.1016/j.micinf.2020.09.006
  • Jacobson, S. H., Hall, S. N., & Swisher, J. R. (2006). Discrete-event simulation of health care systems. patient Flow: Reducing delay in healthcare delivery. Springer. https://doi.org/10.1007/978-0-387-33636-7_8
  • Johansson, M. A., Quandelacy, T. M., Kada, S., Prasad, P. V., Steele, M., Brooks, J. T., Slayton, R. B., Biggerstaff, M., & Butler, J. C. (2021). SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA Network Open, 4(1), e2035057. https://doi.org/10.1001/jamanetworkopen.2020.35057
  • Jones, W., Kotiadis, K., & O’hanley, J. (2019). Engaging stakeholders to extend the life cycle of hybrid simulation models. Proceedings of the 2019 Winter Simulation Conference, 1304–1315. National Harbor, MD, USA. [https://doi.org/10.1109/WSC40007.2019.9004744]
  • Jones, W., Kotiadis, K., O’hanley, J. R., & Robinson, S. (2021). Aiding the development of the conceptual model for hybrid simulation: Representing the modelling frame. Journal of Operational Research Society, 1–20. https://doi.org/10.1080/01605682.2021.2018368
  • Jun, J., Jacobson, S., & Swisher, J. (1999). Applications of discrete event simulation in health care clinics: A survey. The Journal of the Operational Research Society, 50(2), 109–123. https://doi.org/10.1057/palgrave.jors.2600669
  • Kang, B. G., Park, H. -M., Jang, M., & Seo, K. -M. (2021). Hybrid model-based simulation analysis on the effects of social distancing policy of the COVID-19 epidemic. International Journal of Environmental Research and Public Health, 18(21), 11264. https://doi.org/10.3390/ijerph182111264
  • Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov Chain. Annals of Mathematical Statistics, 24(3), 338–354. https://doi.org/10.1214/aoms/1177728975
  • Kierzkowski, A., & Kisiel, T. (2020). Simulation model of security control lane operation in the state of the COVID-19 epidemic. Journal of Air Transport Management, 88, 101868. https://doi.org/10.1016/j.jairtraman.2020.101868
  • Kim, Y., Ryu, H., & Lee, S. (2018). Agent-based modeling for superspreading events: A case study of MERS-Cov transmission dynamics in the Republic of Korea. International Journal of Environmental Research and Public Health, 5(11), 2369. https://doi.org/10.3390/ijerph15112369
  • Körner, A. (2016). Hybrid modelling in system simulation. International Journal of Business and Technology, 4(2), 1–4. https://doi.org/10.33107/ijbte.2016.4.2.01
  • Kuhlman, C. J., Ren, Y., Lewis, B., & Schlitt, J. (2017). Hybrid agent-based modeling of Zika in the United States. Proceeedings of 2017 Winter Simulation Conference, 3-6 Dec. 2017, Las Vegas, NV, USA. [https://doi.org/10.1109/WSC.2017.8247857]
  • Kwok, K. O., Tang, A., Wei, V. W., Park, W. H., Yeoh, E. K., & Riley, S. (2019). Epidemic models of contact tracing: Systematic review of transmission studies of severe acute respiratory syndrome and middle east respiratory syndrome. Computational and Structural Biotechnology Journal, 17, 186–194. https://doi.org/10.1016/j.csbj.2019.01.003
  • Laskowski, M., Demianyk, B. C. P., Witt, J., Mukhi, S. N., Friesen, M. R., & McLeod, R. D. (2011). Agent-based modeling of the spread of influenza-like illness in an emergency department: A simulation study. IEEE Transactions on Information Technology in Biomedicine, 15(6), 877–889. https://doi.org/10.1109/TITB.2011.2163414
  • Lättilä, L., Hilletofth, P., & Lin, B. (2010). Hybrid simulation models – When, Why, How? Expert Systems with Applications, 37(12), 7969–7975. https://doi.org/10.1016/j.eswa.2010.04.039
  • Laxminarayan, R., Wahl, B., Dudala, S. R., Gopal, K., Mohan, B. C., Neelima, S., Jawahar Reddy, K. S., Radhakrishnan, J., & Lewnard, J. A. (2020). Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science, 370(6517), 691–697. https://doi.org/10.1126/science.abd7672
  • Lenormand, M., Louail, T., Cantú-Ros, O. G., Picornell, M., Herranz, R., Arias, J. M., Barthelemy, M., Miguel, M. S., & Ramasco, J. J. (2015). Influence of sociodemographic characteristics on human mobility. Scientific Reports, 5(1), 10075. https://doi.org/10.1038/srep12188
  • Li, G., Li, W., He, X., & Cao, Y. (2020). Asymptomatic and presymptomatic infectors: Hidden sources of COVID-19 disease. Clinical Infectious Diseases, 71(8), 2018. https://doi.org/10.1093/cid/ciaa418
  • Linnéusson, G., Ng, A. H. C., & Aslam, T. (2020). A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance. European Journal of Operational Research, 281(2), 402–414. https://doi.org/10.1016/j.ejor.2019.08.036
  • Li, T., Rong, L., & Zhang, A. (2021). Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail. Transport Policy (Oxf), 106, 226–238. https://doi.org/10.1016/j.tranpol.2021.04.009
  • Liu, D., & Deng, X. (2020). Investigating the strategy on path planning on aircraft evacuation process using discrete event simulation. Mobile Networks and Applications, 26(2), 736–744. https://doi.org/10.1007/s11036-019-01416-2
  • Liu, F. C., Enanoria, W. T. A., Zipprich, J., Blumberg, S., Harriman, K., Ackley, S. F., Wheaton, W. D., Allpress, J. L., & Porco, T. C. (2015). The role of vaccination coverage, individual behaviors, and the public health response in the control of measles epidemics: An agent-based simulation for California. BMC Public Health, 15(1), 447. https://doi.org/10.1186/s12889-015-1766-6
  • Liu, J., Hu, L., Xu, X., & Wu, J. (2021). A queuing network simulation optimization method for coordination control of passenger flow in urban rail transit stations. Neural Computing & Applications, 33(1), 1–25. https://doi.org/10.1007/s00521-020-05580-5
  • Liu, S., Li, Y., Triantis, K. P., Xue, H., & Wang, Y. (2020). The diffusion of discrete event simulation approaches in health care management in the past four decades: A comprehensive review. MDM Policy & Practice, 5(1), 2381468320915242. https://doi.org/10.1177/2381468320915242
  • Liu, S., Triantis, K. P., Zhao, L., Wang, Y., & Deng, Y. (2018a). Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research. PLoS One, 13(4), e0194687. https://doi.org/10.1371/journal.pone.0194687
  • Liu, S., Xue, H., Li, Y., Xu, J., & Wang, Y. (2018b). Investigating the diffusion of agent‐based modelling and system dynamics modelling in population health and healthcare research. Systems Research and Behavioral Science, 35(2), 203–215. https://doi.org/10.1002/sres.2460
  • Lu, Y., Guan, Y., Zhong, X., Fishe, J. N., & Hogan, T. (2021). Hospital beds planning and admission control policies for COVID-19 pandemic: A hybrid computer simulation approach. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 956–961. Lyon, France. [https://doi.org/10.1109/CASE49439.2021.9551589]
  • Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28(1), 143–166. https://doi.org/10.1146/annurev.soc.28.110601.141117
  • Maneerat, S., & Daudé, É. (2016). A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas. Ecological modelling, 333, 66–78.
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491
  • Mielczarek, B., & Zabawa, J. (2016). Modeling healthcare demand using a hybrid simulation approach. Proceedings of the 2016 Winter Simulation Conference (WSC), 1535–1549. Washington, DC, USA. [https://doi.org/10.1109/WSC.2016.7822204]
  • Mokhtari, A., Mineo, C., Kriseman, J., Kremer, P., Neal, L., & Larson, J. (2021). A multi‑method approach to modeling COVID‑19 disease dynamics in the United States. Scientific Reports, 11(1), 12426. https://doi.org/10.1038/s41598-021-92000-w
  • Morgan, J. S., Howick, S., & Belton, V. (2017). A toolkit of designs for mixing discrete event simulation and system dynamics. European Journal of Operational Research, 257(3), 907–918. https://doi.org/10.1016/j.ejor.2016.08.016
  • Mustafee, N., Brailsford, S., Djanatliev, A., Eldabi, T., Kunc, M., & Tolk, A. (2017). Purpose and benefits of hybrid simulation: Contributing to the convergence of its definition. Proceedings of the 2017 Winter Simulation Conference (WSC), 1631–1645. Las Vegas, NV, USA. [https://doi.org/10.1109/WSC.2017.8247903]
  • Mustafee, N., Mittal, S., Diallo, S., & Zacharewicz, G. (2018). Hybrid systems modeling. Simulation: Transactions of the Society for Modeling and Simulation International, Editorial, 94(3), 177–178. https://doi.org/10.1177/0037549718758428
  • Mustafee, N., & Powell, J. H. (2018). From hybrid simulation to hybrid systems modelling. Proceedings of the 2018 Winter Simulation Conference(WSC), 1430–1439. Gothenburg, Sweden. [https://doi.org/10.1109/WSC.2018.8632528]
  • Mykoniatis, K., Angelopoulou, A., & Smith, T. G. (2021). Assessing escalator pedestrian traffic dynamics amid COVID-19 pandemic using hybrid simulation. International Journal of Simulation and Process Modelling, 16(3), 237–246. https://doi.org/10.1504/IJSPM.2021.117332
  • Na, H. S., & Banerjee, A. (2019). Agent-based discrete-event simulation model for no-notice natural disaster evacuation planning. Computers & Industrial Engineering, 129, 44–55. https://doi.org/10.1016/j.cie.2019.01.022
  • Nakata, Y., & Röst, G. (2015). Global analysis for spread of infectious diseases via transportation networks. Journal of Mathematical Biololgy, 70(6), 1411–1456. https://doi.org/10.1007/s00285-014-0801-z
  • Nguyen, L. K. N., Megiddo, I., & Howick, S. (2021). Hybrid simulation for modeling healthcare-associated infections: Promising but challenging. Clinical Infectious Diseases, 72(8), 1475–1480. https://doi.org/10.1093/cid/ciaa1276
  • Nguyen, L. K. N., Megiddo, I., Howick, S., & Struchiner, C. J. (2022). Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff. PLoS Computational Biology, 18(1), e1009780. https://doi.org/10.1371/journal.pcbi.1009780
  • Oleghe, O., & Salonitis, K. (2019). Hybrid simulation modelling of the human-production process interface in lean manufacturing systems. International Journal of Lean Six Sigma, 10(2), 665–690. https://doi.org/10.1108/IJLSS-01-2018-0004
  • Olsen, S. J., Chang, H. -L., Cheung, T. -Y., Tang, A. -Y., Fisk, T. L., Ooi, S.P. -L., Kuo, H. -W., Jiang, D. -S., Chen, K. -T., Lando, J., Hsu, K. -H., Chen, T. -J., & Dowell, S. F. (2003). Transmission of the severe acute respiratory syndrome on aircraft. The New England Journal of Medicine, 349(25), 2416–2422. https://doi.org/10.1056/nejmoa031349
  • O’neil, C. A., & Sattenspiel, L. (2010). Agent-based modeling of the spread of the 1918-1919 flu in three Canadian fur trading communities. American Journal of Human Biology, 22(6), 757–767. https://doi.org/10.1002/ajhb.21077
  • Oran, D. P., & Topol, E. J. (2020). Prevalence of asymptomatic SARS-CoV-2 infection: A narrative review. Annals of Internal Medicine, 173(5), 362–367. https://doi.org/10.7326/M20-3012
  • Peak, C. M., Childs, L. M., Grad, Y. H., & Buckee, C. O. (2017). Comparing nonpharmaceutical interventions for containing emerging epidemics. Proceedings of the National Academy of Sciences, 114(15), 4023–4028. https://doi.org/10.1073/pnas.1616438114
  • Penny, K. E. E., Bayer, S., & Brailsford, S. (2022). A hybrid simulation approach for planning health and social care services. Journal of Simulation, 1–15. https://doi.org/10.1080/17477778.2022.2035275
  • Perez, L., & Dragicevic, S. (2009). An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 8(1), 50. https://doi.org/10.1186/1476-072X-8-50
  • Perkins, T. A., Reiner, R. C., Jr., España, G., ten Bosch, Q. A., Verma, A., Liebman, K. A., Paz-Soldan, V. A., Elder, J. P., Morrison, A. C., Stoddard, S. T., Kitron, U., Vazquez-Prokopec, G. M., Scott, T. W., Smith, D. L., & Alizon, S. (2019). An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections. PLoS Computational Biology, 15(3), e1006710. https://doi.org/10.1371/journal.pcbi.1006710
  • Pluchinoa, A., Garofalob, C., Inturric, G., Rapisardaa, A., & Ignaccoloc, M. (2014). Agent-based simulation of pedestrian behaviour in closed spaces: A museum case study. Journal of Artificial Societies and Social Simulation, 17(1), 16. https://doi.org/10.18564/jasss.2336
  • Possik, J., Gorecki, S., Asgary, A., Solis, A. O., Zacharewicz, G., Tofighi, M., Shafiee, M. A., Merchant, A. A., Aarabi, M., Guimaraes, A., & Nadri, N. (2021). A distributed simulation approach to integrate AnyLogic and unity for virtual reality applications: Case of COVID-19 modelling and training in a dialysis unit. 25th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2021, 1–7. Valencia, Spain. [https://doi.org/10.1109/DS-RT52167.2021.9576149]
  • Prieto, D., & Das, T. K. (2014). An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks. Health Care Management Science, 19(1), 1–19. https://doi.org/10.1007/s10729-014-9273-3
  • Pruckner, M., & German, R. (2013) A hybrid simulation model for large-scaled electricity generation systems. Proceedings of the 2013 Winter Simulation Conference, 1881–1892. Washington, DC, USA. [https://doi.org/10.1109/WSC.2013.6721568]
  • Qiu, H., Chen, Y., Ding, S., Yi, W., Lv, R., & Wang, C. (2021). An improved agent-based model using discrete event simulation for nonpharmaceutical interventions. IEEE Access, 9, 143721–143733. https://doi.org/10.1109/ACCESS.2021.3114226
  • Qu, X., Jackson, M. A., & Davis, L. B. (2012). Simulation-based evaluation of port emergency evacuation plans for predictable natural disasters. Journal of Homeland Security and Emergency Management, 2005. 9(2), 1547–7355. https://doi.org/10.1515/1547-7355.2005
  • Rabelo, L., Sarmiento, A. T., Helal, M., & Jones, A. (2015). Supply chain and hybrid simulation in the hierarchical enterprise. International Journal of Computer Integrated Manufacturing, 28(5), 488–500. https://doi.org/10.1080/0951192X.2014.880807
  • Rafferty, E., McDonald, W., Qian, W. C., Osgood, N. D., & Doroshenko, A. (2018). Evaluation of the effect of chickenpox vaccination on shingles epidemiology using agent-based modeling. PeerJ, 6, e5012. https://doi.org/10.7717/peerj.5012
  • Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. ACM SIGGRAPH Computer Graphics, 21(4), 25–34. https://doi.org/10.1145/37402.37406
  • Roberts, M. (2021). What are the India, Brazil, South Africa and UK variants?. BBC News Online. Retrieved May 6th, 2021, from [https://www.bbc.com/news/health-55659820]
  • Sanstead, E., Kenyon, C., Rowley, S., Enns, E., Miller, C., Ehresmann, K., & Kulasingam, S. (2015). Understanding trends in pertussis incidence: An agent-based model approach. American Journal of Public Health, 105(9), 42–47. https://doi.org/10.2105/AJPH.2015.302794
  • Sayampanathan, A. A., Heng, C. S., Pin, P. H., Pang, J., Leong, T. Y., & Lee, V. J. (2021). Infectivity of asymptomatic versus symptomatic COVID-19. The Lancet Correspondence, 397(10269), 93–94. https://doi.org/10.1016/S0140-6736(20)32651-9
  • Sen, R. P. (2010). Operations research: Algorithms and applications. New Delhi: PHI Learning Private Limited (pp. 518).
  • Shen, J., Duan, H., Zhang, B., Wang, J., Ji, J. S., Wang, J., Pan, L., Wang, X., Zhao, K., Ying, B., Tang, S., Zhang, J., Liang, C., Sun, H., Lv, Y., Li, Y., Li, T., Li, L., Liu, H. … Shi, X. (2020). Prevention and control of COVID-19 in public transportation: Experience from China. Environmental Pollution, 266(Pt 2), 115291. https://doi.org/10.1016/j.envpol.2020.115291
  • Shiwakoti, N., Tay, R., Stasinopoulos, P., & Woolleya, P. J. (2017). Likely behaviours of passengers under emergency evacuation in train station. Safety Science, 91, 40–48. https://doi.org/10.1016/j.ssci.2016.07.017
  • Siettos, C., Anastassopoulou, C., Russo, L., Grigoras, C., & Mylonakis, E. (2015). Modeling the 2014 Ebola virus epidemic – agent-based simulations, temporal analysis and future predictions for Liberia and Sierra Leone. PLoS Currents, 7. https://doi.org/10.1371/currents.outbreaks.8d5984114855fc425e699e1a18cdc6c9
  • Silva, P. C. L., Batista, P. V. C., Lima, H. S., Alves, M. A., Guimarães, F. G., & Silva, R. C. P. (2020). COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos, Solitons & Fractals, 139, 110088. https://doi.org/10.1016/j.chaos.2020.110088
  • Smaldinoa, P., Pickettb, C., Shermanb, J., & Schankb, J. (2012). An agent-based model of social identity dynamics. Journal of Artificial Societies and Social Simulation, 15(4), 7. https://doi.org/10.18564/jasss.2030
  • Smith, N. R., Trauer, J. M., Gambhir, M., Richards, J. S., Maude, R. J., Keith, J. M., & Flegg, J. A. (2018). Agent-based models of malaria transmission: A systematic review. Malaria journal, 17(1), 299. https://doi.org/10.1186/s12936-018-2442-y
  • Stapelberg, N. J. C., Smoll, N. R., Randall, M., Palipana, D., Bui, B., Macartney, K., Khandaker, G., Wattiaux, A., & Ndeffo Mbah, M. L. (2021). A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian cities. PLoS One, 16(5), e0251737. https://doi.org/10.1371/journal.pone.0251737
  • Sun, C., & Zhai, Z. (2020). The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustainable Cities and Society, 62, 102390]. https://doi.org/10.1016/j.scs.2020.102390
  • Tatapudi, H., Das, R., & Das, T. K. (2020). Impact assessment of full and partial stay-at-home orders, face mask usage, and contact tracing: An agent-based simulation study of COVID-19 for an urban region. Global Epidemiology, 2, 100036. https://doi.org/10.1016/j.gloepi.2020.100036
  • Tejada, J. J., Ivy, J. S., King, R. E., Wilson, J. R., Ballan, M. J., Kay, M. G., Diehl, K. M., & Yankaskas, B. C. (2014). Combined DES/SD model of breast cancer screening for older women, II: Screening-and-treatment simulation. IIE Transactions, 46(7), 707–727. https://doi.org/10.1080/0740817X.2013.851436
  • Tofighi, M., Asgary, A., Merchant, A. A., Shafiee, M. A., Najafabadi, M. M., Nadri, N., Aarabi, M., Heffernan, J., Wu, J., & Provenzano, M. (2021). Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices. PLoS One, 16(11), e0259970. https://doi.org/10.1371/journal.pone.0259970
  • Tolk, A., Harper, A., & Mustafee, N. (2021). Hybrid models as transdisciplinary research enablers. European Journal of Operational Research, 291(3), 1075–1090. https://doi.org/10.1016/j.ejor.2020.10.010
  • Tracy, M., Cerdá, M., & Keyes, K. M. (2018). Agent-based modeling in public health: Current applications and future directions. Annual Review of Public Health, 39(1), 77–94. https://doi.org/10.1146/annurev-publhealth-040617-014317
  • Tran, K., Cimon, K., Severn, M., Pessoa-Silva, C. L., Conly, J., & Semple, M. G. (2012). Aerosol generating procedures and risk of transmission of acute respiratory infections to healthcare workers: A systematic review. PLoS One, 7(4), e35797. https://doi.org/10.1371/journal.pone.0035797pmid:22563403
  • Ullah, S., & Khan, M. A. (2020). Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study. Chaos, Solitons & Fractals, 139, 110075. https://doi.org/10.1016/j.chaos.2020.110075
  • Venkatramanan, S., Lewis, B., Chen, J., Higdon, D., Vullikanti, A., & Marathe, M. (2018). Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics, 22, 43–49. https://doi.org/10.1016/j.epidem.2017.02.010
  • Vermeulen, K., Thas, O., & Vansteelandt, S. (2015). Increasing the power of the Mann-Whitney test in randomized experiments through flexible covariate adjustment. Statistics in Medicine, 34(6), 1012–1030. https://doi.org/10.1002/sim.6386
  • Viana, J., Simonsen, T. B., Faraas, H. E., Schmidt, N., Dahl, F. A., & Flo, K. (2020). Capacity and patient flow planning in post-term pregnancy outpatient clinics: A computer simulation modelling study. BMC Health Services Research, 20(1), 1–15. https://doi.org/10.1186/s12913-020-4943-y
  • Von Neumann, J. (1951). The general and logical theory of automata. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior – the Hixon symposium (pp. 1–31). John Wiley and Sons.
  • Vuorinen, V., Aarnio, M., Alava, M., Alopaeus, V., Atanasova, N., Auvinen, M., Balasubramanian, N., Bordbar, H., Erästö, P., Grande, R., Hayward, N., Hellsten, A., Hostikka, S., Hokkanen, J., Kaario, O., Karvinen, A., Kivistö, I., Korhonen, M., Kosonen, R. … Österberg, M. (2020). Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors. Safety Science, 130, 104866. https://doi.org/10.1016/j.ssci.2020.104866
  • Waleed, M., Um, T. W., Kamal, T., Khan, A., & Zahid, Z. U. (2020). SIM-D: An agent-based simulator for modeling contagion in population. Applied Sciences, 10(21), 7745. https://doi.org/10.3390/app10217745
  • Wang, B., Brême, S., & Moon, Y. B. (2014). Hybrid modeling and simulation for complementing lifecycle assessment. Computers & Industrial Engineering, 69, 77–88. https://doi.org/10.1016/j.cie.2013.12.016
  • Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. The Lancet, 395(10223), 470–473. https://doi.org/10.1016/S0140-6736(20)30185-9
  • Warde, P. R., Patel, S. S., Ferreira, T. D., Gershengorn, H. B., Bhatia, M. C., Parekh, D. J., Manni, K. J., & Shukla, B. S. (2021). Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ Health Care Informatics, 28(1), e100248. https://doi.org/10.1136/bmjhci-2020-100248
  • WHO: World Health Organization. (2022a). WHO Coronavirus (COVID-19) dashboard. Retrieved January 17 , 2021, from [https://covid19.who.int/]
  • WHO:World Health Organization. (2022b). The effects of virus variants on COVID-19 vaccines. Retrieved May 1, 2021, from [https://www.who.int/news-room/feature-stories/detail/the-effects-of-virus-variants-on-covid-19-vaccines]
  • Willem, L., Verelst, F., Bilcke, J., Hens, N., & Beutels, P. (2017). Lessons from a decade of individual-based models for infectious disease transmission: A systematic review (2006-2015). BMC Infectious Diseases, 17(1), 612. https://doi.org/10.1186/s12879-017-2699-8
  • Wilson, N., Corbett, S., & Tovey, E. (2020). Airborne transmission of covid-19: Guidelines and governments must acknowledge the evidence and take steps to protect the public. The BMJ, 370, m3206. https://doi.org/10.1136/bmj.m3206
  • Wu, J., Tang, B., Bragazzi, N. L., Nah, K., & McCarthy, Z. (2020). Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada. Journal of Mathematics Industry, 10(1), 15. https://doi.org/10.1186/s13362-020-00083-3
  • Xiao, H., Tian, H. Y., Zhao, J., Zhang, X. X., Li, Y. P., Liu, Y., Liu, R. C., & Chen, T. M. (2011). Influenza a (H1N1) transmission by road traffic between cities and towns. Chinese Science Bulletin, 56(24), 2613–2620. https://doi.org/10.1007/s11434-011-4598-5
  • Xu, F., McCluskey, C. C., & Cressman, R. (2013). Spatial spread of an epidemic through public transportation systems with a hub. Mathematical Biosciences, 246(1), 164–175. https://doi.org/10.1016/j.mbs.2013.08.014
  • Yazdani, D., Omidvar, M. N., Deplano, I., Lersteau, C., Makki, A., Wang, J., & Nguyen, T. T. (2019). Real-time seat allocation for minimizing boarding/alighting time and improving quality of service and safety for passengers. Transportation Research Part C: Emerging Technologies, 103, 158–173. https://doi.org/10.1016/j.trc.2019.03.014
  • Zhang, B., Chan, W. K. V., & Ukkusuri, S. V. (2014). On the modelling of transportation evacuation: An agent-based discrete-event hybrid-space approach. Journal of Simulation, 8(4), 259–270. https://doi.org/10.1057/jos.2014.3
  • Zhang, W., Liu, S., Osgood, N., Zhu, H., Qian, Y., & Jia, J. Using simulation modelling and systems science to help contain COVID-19: A systematic review. (2022). Systems Research and Behavioral Science, 40(1), 1–28. https://doi.org/10.1002/sres.2897
  • Zhang, Y., Zhang, A., & Wang, J. (2020). Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China. Transport Policy (Oxf), 94, 34–42. https://doi.org/10.1016/j.tranpol.2020.05.012
  • Zhao, S., Zhuang, Z., Ran, J., Lin, J., Yang, G., Yang, L., & He, D. (2020). The association between domestic train transportation and novel coronavirus (2019-nCov) outbreak in China from 2019 to 2020: A data-driven correlational report. Travel Medicine and Infectious Diseases, 33, 101568. https://doi.org/10.1016/j.tmaid.2020.101568
  • Zoellner, C., Jennings, R., Wiedmann, M., & Ivanek, R. (2019). EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities. Scientific Reports, 9(1), 495. https://doi.org/10.1038/s41598-018-36654-z
  • Zohdi, T. I. (2020). An agent-based computational framework for simulation of global pandemic and social response on planet X. Computational Mechanics, 66(5), 1195–1209. https://doi.org/10.1007/s00466-020-01886-2
  • Zulkepli, J., Eldabi, T., & Mustafee, N. (2012). Hybrid simulation for modelling large systems: An example of integrated care model. Proceedings of the 2012 Winter Simulation Conference (WSC), 758–769. Berlin, Germany. [https://doi.org/10.1109/WSC.2012.6465314]

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.