Hybrid modelling and simulation of the effect of vaccination on the COVID-19 transmission

References

• Anastassopoulou, C., Russo, L., Tsakris, A., Siettos, C., & Othumpangat, S. (2020). Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE, 15(3), e0230405. https://doi.org/10.1371/journal.pone.0230405
   PubMed Web of Science ®Google Scholar
• Annas, S., Pratama, M. I., Rifandi, M., Sanusi, W., & Side, S. (2020). Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia. Chaos, Solitons & Fractals, 139, 110072. https://doi.org/10.1016/j.chaos.2020.110072
   PubMed Web of Science ®Google Scholar
• Apenteng, O. O., & Ismail, N. A. (2017). Modelling the spread of HIV and AIDS epidemic trends in male and female populations. World Journal of Modelling and Simulation, 13(3), 183–192.
   Google Scholar
• 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.
   Google Scholar
• Bo, Y., Guo, C., Lin, C., Zeng, Y., Li, H. B., Zhang, Y., Lao, X. Q., Chan, J. W. M., Yeung, D. W., Kwok, K. O., Wong, S. Y. S., Lau, A. K. H., & Lao, X. Q. (2021). Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23. International Journal of Infectious Diseases, 102, 247–253. https://doi.org/10.1016/j.ijid.2020.10.066. January to 13 April 2020.
   PubMed Web of Science ®Google Scholar
• Brailsford, S. C. (2015, December). Hybrid simulation in healthcare: New concepts and new tools. In 2015 winter simulation conference (WSC). Huntington Beach, CA, USA (pp. 1645–1653). IEEE.
   Google Scholar
• Brailsford, S. C., Eldabi, T., Kunc, M., Mustafee, N., & Osorio, A. F. (2019). 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
   Web of Science ®Google Scholar
• Byrne, A. W., McEvoy, D., Collins, A. B., Hunt, K., Casey, M., Barber, A., More, S. J., Griffin, J., Lane, E. A., McAloon, C., O’Brien, K., Wall, P., Walsh, K. A., & More, S. J. (2020). Inferred duration of infectious period of SARS-CoV-2: Rapid scoping review and analysis of available evidence for asymptomatic and symptomatic COVID-19 cases. BMJ open, 10(8), e039856. https://doi.org/10.1136/bmjopen-2020-039856
   PubMed Web of Science ®Google Scholar
• Choi, S., & Ki, M. (2020). Estimating the reproductive number and the outbreak size of COVID-19 in Korea. Epidemiology and health, 42, 1–10.
   Google Scholar
• Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang, C. B., & Bernardini, S. (2020). The COVID-19 pandemic. Critical Reviews in Clinical Laboratory Sciences, 57(6), 365–388. https://doi.org/10.1080/10408363.2020.1783198
   PubMed Web of Science ®Google Scholar
• Currie, C. S., 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
   Web of Science ®Google Scholar
• Dan, J. M., Mateus, J., Kato, Y., Hastie, K. M., Yu, E. D., Faliti, C. E., Crotty, S., Ramirez, S. I., Haupt, S., Frazier, A., Nakao, C., Rayaprolu, V., Rawlings, S. A., Peters, B., Krammer, F., Simon, V., Saphire, E. O., Smith, D. M., Weiskopf, D., and Crotty, S. (2021). Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science, 371(6529), eabf4063. https://doi.org/10.1126/science.abf4063
   PubMed Web of Science ®Google Scholar
• Dos Santos, V. H., Kotiadis, K., & Scaparra, M. P. (2020, December). A review of hybrid simulation in healthcare. In 2020 Winter Simulation Conference (WSC) (pp. 1004–1015). IEEE.
   Google Scholar
• Ferguson, M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., & Baguelin, M. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand, Imperial College COVID-19 Response Team. 16 March 2020.
   Google Scholar
• Gudbjartsson, D. F., Norddahl, G. L., Melsted, P., Gunnarsdottir, K., Holm, H., Eythorsson, E., Stefansson, K., Helgason, D., Bjarnadottir, K., Ingvarsson, R. F., Thorsteinsdottir, B., Kristjansdottir, S., Birgisdottir, K., Kristinsdottir, A. M., Sigurdsson, M. I., Arnadottir, G. A., Ivarsdottir, E. V., Andresdottir, M., Jonsson, F., … Stefansson, K. (2020). Humoral immune response to SARS-CoV-2 in Iceland. New England Journal of Medicine, 383(18), 1724–1734. https://doi.org/10.1056/NEJMoa2026116
   PubMed Web of Science ®Google Scholar
• Hamer, W. H. (1906). Epidemic disease in England. Lancet, 733–739.
   Google Scholar
• Hossain, N. U. I., Debusk, H., & Hasan, M. M. (2017). Reducing patient waiting time in an outpatient clinic: A discrete event simulation (DES) based approach. In IIE Annual Conference. Proceedings. Pittsburgh, Pennsylvania, USA (pp. 241–246). Institute of Industrial and Systems Engineers (IISE).
   Google Scholar
• Ivorra, B., Ferrández, M. R., Vela-Pérez, M., & Ramos, A. M. (2020). Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China. Communications in Nonlinear Science and Numerical Simulation, 88(105303). https://doi.org/10.1016/j.cnsns.2020.105303
   PubMedGoogle Scholar
• Jarvis, C. I., Van Zandvoort, K., Gimma, A., Prem, K., Klepac, P., Rubin, G. J., & Edmunds, W. J. (2020). Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Medicine, 18(1), 1–10. https://doi.org/10.1186/s12916-020-01597-8
   PubMed Web of Science ®Google Scholar
• Johansson, M. A., Quandelacy, T. M., Kada, S., Prasad, P. V., Steele, M., Brooks, J. T., Butler, J. C., Biggerstaff, M., & Butler, J. C. (2021). SARS-CoV-2 transmission from people without COVID-19 symptoms. JAMA network open, 4 (1), e2035057–e2035057. JAMA network open. https://doi.org/10.1001/jamanetworkopen.2020.35057
   PubMed Web of Science ®Google Scholar
• Khalili, M., Karamouzian, M., Nasiri, N., Javadi, S., Mirzazadeh, A., & Sharifi, H. (2020). Epidemiological characteristics of COVID-19: A systematic review and meta-analysis. Epidemiology & Infection, 148, E130. https://doi.org/10.1017/S0950268820001430
   PubMed Web of Science ®Google Scholar
• Levin, A. T., Hanage, W. P., Owusu-Boaitey, N., Cochran, K. B., Walsh, S. P., & Meyerowitz-Katz, G. (2020). Assessing the age specificity of infection fatality rates for COVID-19: Systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology, 35(12), 1123–1138. https://doi.org/10.1007/s10654-020-00698-1
   PubMed Web of Science ®Google Scholar
• Liu, S., Xue, H., Li, Y., Xu, J., & Wang, Y. (2018). 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
   Web of Science ®Google Scholar
• Lou, J., & Ruggeri, T. (2010). The dynamics of spreading and immune strategies of sexually transmitted diseases on scale-free network. Journal of Mathematical Analysis and Applications, 365(1), 210–219. https://doi.org/10.1016/j.jmaa.2009.10.044
   Web of Science ®Google Scholar
• Luo, L., Liu, D., Liao, X., Wu, X., Jing, Q., Zheng, J., Mao, C., Yang, S., Bi, H., Li, Z., Liu, J., Song, W., Zhu, W., Wang, Z., Zhang, X., Huang, Q., Chen, P., Liu, H., Cheng, X., … Mao, C. (2020). Contact settings and risk for transmission in 3410 close contacts of patients with COVID-19 in Guangzhou, China: A prospective cohort study. Annals of Internal Medicine, 173(11), 879–887. https://doi.org/10.7326/M20-2671
   PubMed Web of Science ®Google Scholar
• Macal, C., & North, M. (2014, December). Introductory tutorial: Agent-based modeling and simulation. In Proceedings of the winter simulation conference 2014. Savannah, GA, USA (pp. 6–20). IEEE.
   Google Scholar
• Mahmood, I., Arabnejad, H., Suleimenova, D., Sassoon, I., Marshan, A., Serrano-Rico, A., Groen, D., Anagnostou, A., Taylor, S. J. E., Bell, D., & Groen, D. (2020). FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions. Journal of Simulation, 1–19. https://doi.org/10.1080/17477778.2020.1800422
   Web of Science ®Google Scholar
• Maugeri, A., Barchitta, M., Battiato, S., & Agodi, A. (2020). Estimation of unreported novel coronavirus (SARS-CoV-2) infections from reported deaths: A susceptible–exposed–Infectious–recovered–dead model. Journal of Clinical Medicine, 9(5), 1350. https://doi.org/10.3390/jcm9051350
   PubMed Web of Science ®Google Scholar
• May, T., & Silverman, R. D. (2003). ‘Clustering of exemptions’ as a collective action threat to herd immunity. Vaccine, 21(11–12), 1048–1051. https://doi.org/10.1016/S0264-410X(02)00627-8
   PubMed Web of Science ®Google Scholar
• McCarthy, J. (2020). Three in Four in U.S. have self-isolated in their household, Gallup. Retrieved on February 27, 2021. Available from: https://news.gallup.com/poll/307760/three-four-self-isolated-household.aspx
   Google Scholar
• Miller, G., Randolph, S., & Patterson, J. E. (2008). Responding to simulated pandemic influenza in San Antonio, Texas. Infection Control & Hospital Epidemiology, 29(4), 320–326. https://doi.org/10.1086/529212
   PubMed Web of Science ®Google Scholar
• Moghadas, S. M., Shoukat, A., Espindola, A. L., Pereira, R. S., Abdirizak, F., Laskowski, M., Viboud, C., & Chowell, G. (2017). Asymptomatic transmission and the dynamics of zika infection. Scientific Reports, 7(1), 1–8. https://doi.org/10.1038/s41598-017-05013-9
   PubMedGoogle Scholar
• Monks, T., Currie, C. S. M., Onggo, B. S., Robinson, S., Kunc, M., & Taylor, S. J. E. (2019). Strengthening the reporting of empirical simulation studies: Introducing the stress guide- lines. Journal of Simulation, 13(1), 55–67.
   Web of Science ®Google Scholar
• Mustafee, N., Powell, J., Brailsford, S. C., Diallo, S., Padilla, J., & Tolk, A. (2015a, December). Hybrid simulation studies and hybrid simulation systems: Definitions, challenges, and benefits. In 2015 Winter Simulation Conference (WSC). Huntington Beach, CA, USA (pp. 1678–1692). IEEE.
   Google Scholar
• Mustafee, N., Sahnoun, M. H., Smart, A., Godsiff, P., Baudry, D., & Louis, A. (2015b, April). Investigating execution strategies for hybrid models developed using multiple M&S methodologies. In Proceedings of the 48th annual simulation symposium. Alexandria, Virginia (pp. 78–85. Society for Computer Simulation International.
   Google Scholar
• Mykoniatis, K. (2015). A generic framework for multi-method modeling and simulation of complex systems using discrete event, system dynamics and agent based approaches.
   Google Scholar
• Mykoniatis, K., & Angelopoulou, A. (2020). A modeling framework for the application of multi-paradigm simulation methods. SIMULATION, 96(1), 55–73. https://doi.org/10.1177/0037549719843339
   Google Scholar
• Prescott, H. C., & Girard, T. D. (2020). Recovery from severe COVID-19: Leveraging the lessons of survival from sepsis. JAMA, 324(8), 739–740. https://doi.org/10.1001/jama.2020.14103
   PubMed Web of Science ®Google Scholar
• Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction. Princeton university press.
   Google Scholar
• Rangkuti, Y. M., Side, S., & Noorani, M. S. M. (2014). Numerical analytic solution of SIR model of dengue fever disease in South Sulawesi using homotopy perturbation method and variational iteration method. Journal of Mathematical and Fundamental Sciences, 46A(1), 91–105. https://doi.org/10.5614/j.math.fund.sci.2014.46.1.8
   Google Scholar
• Ripperger, T. J., Uhrlaub, J. L., Watanabe, M., Wong, R., Castaneda, Y., Pizzato, H. A., Bhattacharya, D., Bradshaw, C., Weinkauf, C. C., Bime, C., Erickson, H. L., Knox, K., Bixby, B., Parthasarathy, S., Chaudhary, S., Natt, B., Cristan, E., El Aini, T., Rischard, F., … Bhattacharya, D. (2020). Orthogonal SARS-CoV-2 serological assays enable surveillance of low-prevalence communities and reveal durable humoral immunity. Immunity, 53(5), 925–933. https://doi.org/10.1016/j.immuni.2020.10.004
   PubMed Web of Science ®Google Scholar
• Ross, R. Report on the Prevention of malaria in mauritius.Waterlow, 1908.
   Google Scholar
• Statista, (July, 2020). “Resident population of the United States by sex and age as of July 1, 2019”. Retrieved on March 15, 2021. Available from: https://www.statista.com/statistics/241488/population-of-the-us-by-sex-and-age/
   Google Scholar
• Syafruddin, S., Mulbar, U., Sidjara, S., & Sanusi, W. (2017) A SEIR Model for transmission of tuberculosis. AIP conference proceedings. (Vol. 1830, no. 1, p. 020004). Bydgoszcz, Poland.
   Google Scholar
• Syafruddin, S., & Noorani, M. S. M. (2013). A SIR model for spread of dengue fever disease (simulation for South Sulawesi Indonesia and Selangor Malaysia. World Journal Modeling Simulation, 9(2), 96–105.
   Google Scholar
• Tang, B., Xia, F., Tang, S., Bragazzi, N. L., Li, Q., Sun, X., & Wu, J. (2020). The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China. International Journal of Infectious Diseases, 95, 288–293.
   PubMed Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Tuan, J., Spichler-Moffarah, A., & Ogbuagu, O. (2021). A new positive SARS-CoV-2 test months after severe COVID-19 illness: Reinfection or intermittent viral shedding? BMJ Case Reports CP, 14(2), e240531. https://doi.org/10.1136/bcr-2020-240531
   PubMedGoogle Scholar
• Tuite, A. R., Fisman, D. N., & Greer, A. L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Canadian Medical Association Journal, 192(19), E497–E505. https://doi.org/10.1503/cmaj.200476
   PubMed Web of Science ®Google Scholar
• Tuomisto, J. T., Yrjölä, J., Kolehmainen, M., Bonsdorff, J., Pekkanen, J., & Tikkanen, T. (2020). An agent-based epidemic model reina for covid-19 to identify destructive policies. medRxiv. https://www.medrxiv.org/content/early/2020/04/17/2020.04.09.20047498
   Google Scholar
• Vega, D. I. (2020). Lockdown, one, two, none, or smart. Modeling containing COVID-19 infection. A conceptual model. Science of the Total Environment, 730, 138917.
   PubMed Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.ejor.2014.02.052
   Web of Science ®Google Scholar
• Villanueva-Oller, J., Acedo, L., Moraño, J. A., & Sánchez-Sánchez, A. (2013, January). Epidemic random network simulations in a distributed computing environment. In Abstract and Applied Analysis (Vol. 2013). Hindawi.
   Google Scholar
• 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(8), 680–681. https://doi.org/10.7326/L20-1062
   PubMed Web of Science ®Google Scholar
• WHO (2020), Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19).Retrieved on February 27, 2021. Available from: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
   Google Scholar
• Wood, R. M., McWilliams, C. J., Thomas, M. J., Bourdeaux, C. P., & Vasilakis, C. (2020). COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care. Health Care Management Science, 23(3), 315–324. https://doi.org/10.1007/s10729-020-09511-7
   PubMed Web of Science ®Google Scholar
• Worldometers (2020), Reported Cases and Deaths. Retrieved on March 18, 2021. Available from: https://www.worldometers.info/coronavirus/
   Google Scholar