Analyzing the Impact of Vaccination on COVID-19 Spread and Hospitalizations: A Multi-Paradigm Simulation Modeling Approach

  • Julia Bitencourt,
  • Mohsen Nikfar,
  • Konstantinos Mykoniatis 
  • a,b,c, Auburn University, Auburn, Alabama, 36849, United States of America
Cite as
Bitencourt J., Nikfar M., Mykoniatis K. (2021). Analyzing the Impact of Vaccination on COVID-19 Spread and Hospitalizations: A Multi-Paradigm Simulation Modeling Approach. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 345-354. DOI:


Multi-paradigm simulation modeling aids the study and analysis of complex systems and their internal interactions. Given the inherent variability that exists in real-world settings, the use of different structures and methods is necessary to accurately represent the system under study. This study integrates Discrete Event Simulation and Agent-Based Modeling, developing a multi-paradigm simulation model to study the emergent COVID-19 crisis. In specific, the goal of this research is to determine how the vaccine distribution affects the spread of COVID-19 as well as hospitalizations for the state of Alabama. The simulation model incorporates three main components, including the supply chain of vaccines, the spread of COVID-19, and hospitalizations. The supply chain of vaccines simulation component studies the availability of trucks for supplying the vaccines and vaccine damage due to inappropriate handling and storage. The spread of the COVID-19 component incorporates the Susceptible Exposed Infected Recovery epidemic model. Lastly, the hospitalizations component considers capacity requirements (in terms of the number of available beds) and treatment times. The multi-paradigm model enables a better understanding of the interactions between variables of interest, helps to evaluate hospital bed requirements, and provides metrics that support the management and control of the epidemic and healthcare system.


  1. A. Borshchev, The Big book of Simulation Modeling, 1st ed., AnyLogic Company XJ Technologies, 2013. 
  2. Alabama Department of Public Health. (2021). Retrieved from:
  3. Angelopoulou, A., & Mykoniatis, K. (2018). UTASiMo: a simulation-based tool for task analysis. Simulation, 94(1), 43-54.
  4. Asgary, A., Najafabadi, M. M., Karsseboom, R., & Wu, J. (2020, December). A drive-through simulation tool for mass vaccination during COVID-19 pandemic. In Healthcare (Vol. 8, No. 4, p. 469). Multidisciplinary Digital Publishing Institute.
  5. Barnhill, C. (2021, January 5). The COVID-19 Vaccine Supply Chain: Potential Problems and Bottlenecks. Retrieved from Poole Thought Leadership:
  6. Bartsch, S. M., Ferguson, M. C., McKinnell, J. A., O'Shea, K. J., Wedlock, P. T., Siegmund, S. S., & Lee, B. Y. (2020). The Potential Health Care Costs And Resource Use Associated With COVID-19 In the US: A simulation estimate of the direct medical costs and health care resource use associated with COVID-19 infections in the US. Health affairs, 39(6), 927-935.
  7. Chahal, K., Eldabi, T., & Young, T. (2013). A conceptual framework for hybrid system dynamics and discrete event simulation for healthcare. Journal of Enterprise Information Management.
  8. Cuevas, E. (2020). An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computers in biology and medicine, 121, 103827.
  9. Das, A. (2020). Impact of the COVID-19 pandemic on the workflow of an ambulatory endoscopy center: an assessment by discrete event simulation. Gastrointestinal endoscopy, 92(4), 914-924.
  10. Dashti, H., Roche, E. C., Bates, D. W., Mora, S., & Demler, O. (2021). SARS2 simplified scores to estimate risk of hospitalization and death among patients with COVID-19. Scientific reports, 11(1), 1-9.
  11. De Paz, J. L., Flores, I. (2014). Simulation optimization for a vaccine distribution strategy against the spread of A(H1N1) epidemic. Proceedings of the 26th European Modeling & Simulation Symposium (EMSS 2014), pp. 120-127.
  12. Djanatliev, A., & German, R. (2013, December). Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation. In 2013 Winter Simulations Conference (WSC) (pp. 270-281). IEEE.
  13. FDA newsletter. (2020, December 11). Retrieved from:
  14. Gnanvi, J., Salako, K. V., Kotanmi, B., & Kakaï, R. G. (2021). On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modeling techniques. Infectious Disease Modeling.
  15. Golan, M. S., Trump, B. D., Cegan, J. C., & Linkov, I. (2020). The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution. arXiv preprint.
  16. Gössling, S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: a rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1-20.
  17. Jalayer, M., Orsenigo, C., & Vercellis, C. (2020). CoV-ABM: A stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19. arXiv preprint arXiv:2007.13231.
  18. John Hopkins Coronavirus Resource Center. (2021a). Retrieved from:
  19. John Hopkins Coronavirus Resource Center. (2021b). Retrieved from:
  20. John Hopkins Coronavirus Resource Center. (2021c). Retrieved from:
  21. Li, J., & Giabbanelli, P. (2021). Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study. JMIR Medical Informatics, 9(4), e27419.
  22. Melman, G. J., Parlikad, A. K., & Cameron, E. A. B. (2021). Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation. Health Care Management Science, 1-19.
  23. Mykoniatis, K. (2015). A Generic Framework For Multi-Method Modeling and Simulation of Complex Systems Using Discrete Event, System Dynamics and Agent Based Approaches.
  24. Mykoniatis, K., & Angelopoulou, A. (2020). A modeling framework for the application of multi-paradigm simulation methods. Simulation, 96(1), 55-73.
  25. Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., ... & Zanos, T. P. (2020). Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. Jama, 323(20), 2052-2059.
  26. Silva, P. C., Batista, P. V., Lima, H. S., Alves, M. A., Guimarães, F. G., & Silva, R. C. (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.x
  27. Sy, C., Bernardo, E., Miguel, A., San Juan, J. L., Mayol, A. P., Ching, P. M., ... & Mutuc, J. E. (2020). Policy development for pandemic response using system dynamics: a case study on COVID-19. Process Integration and Optimization for Sustainability, 4(4), 497-501.
  28. US Security and Exchange Commission. (2021). Retrieved from:
  29. Viana, J., Brailsford, S. C., Harindra, V., & Harper, P. R. (2014). Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection. European Journal of Operational Research, 237(1), 196-206.
  30. Weissman, G. E., Crane-Droesch, A., Chivers, C., Luong, T., Hanish, A., Levy, M. Z., ... & Halpern, S. D. (2020). Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Annals of internal medicine, 173(1), 21-28.
  31. Wood, R. M., McWilliams, C. J., Thomas, M. J., Bourdeaux, C. P., & Vasilakis, C. (2020). COVID-19 scenario modeling for the mitigation of capacity-dependent deaths in intensive care. Health care management science, 23(3), 315-324.