The novel coronavirus disease (COVID-19) pandemic has caused an overwhelming impact on lives around the world. Countries around the world have scrambled to implement various control measures such as social distancing, community lockdowns, quarantines in varying degrees of stringency and success. This paper proposed the application of the system dynamics (SD) modeling framework to capture the complex relationships, feedbacks, and delays present in a disease transmission system so that policies may be developed to effectively target the issue. In this study, three common policies, namely social distancing, quarantine, and vaccination, were integrated into the basic flu model to assess which would be the most effective in mitigating the infection and identify the portion of the system it would be best to leverage actions on. Results revealed that policies that remove the possibility of transmission through quarantine and vaccination performed best in reducing the spread and consequences of the pandemic. This model may help policymakers evaluate potential policy alternatives, especially when responding to high-risk issues such as a pandemic.
Alamerew, Y. A., & Brissaud, D. (2020). Modeling reverse supply chain through system dynamics for realizing the transition towards the circular economy: A case study on electric vehicle batteries. Journal of Cleaner Production, 254, 120025. https://doi.org/10.1016/j.jclepro.2020.120025
Deng, S., Ji, J., Wen, G., & Xu, H. (2020). Delay-induced novel dynamics in a hexagonal centrifugal governor system. International Journal of Non-Linear Mechanics, 121, 103465. https://doi.org/10.1016/j.ijnonlinmec.2020.103465
Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14(1), 3-29. https://doi.org/10.1002/(sici)1099-1727(199821)14:1<3::aid-sdr140>3.0.co;2-k
Ejima, K., & Nishiura, H. (2018). Real-time quantification of the next-generation matrix and age-dependent forecasting of pandemic influenza H1N1 2009 in Japan. Annals of Epidemiology, 28(5), 301-308. https://doi.org/10.1016/j.annepidem.2018.02.010
Grassly, N. C., & Fraser, C. (2008). Mathematical models of infectious disease transmission. Nature Reviews Microbiology, 6(6), 477-487. https://doi.org/10.1038/nrmicro1845
Hart, O. E., & Halden, R. U. (2020). Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities, and challenges. Science of the Total Environment, 138875.
Ibarra-Vega, D. (2020). Lockdown, one, two, none, or smart. Modeling containing COVID-19 infection. A conceptual model. Science of the Total Environment, 730, 138917. https://doi.org/10.1016/j.scitotenv.2020.138917
Thiel, D., Le Hoa Vo, T., & Hovelaque, V. (2014). Forecasts impacts on sanitary risk during a crisis: A case study. International Journal of Logistics Management, 25(2), 358-378. DOI: 10.1108/IJLM-04-2012-0028
World Health Organization. (2020, January 30). IHR emergency committee on novel coronavirus (2019-nCoV). Retrieved from https://www.who.int/dg/speeches/detail/who-director-general-s-statement-on-ihr-emergency-committee-on-novel-coronavirus-(2019-ncov)
Yang, Y., Zhang, H., & Chen, X. (2020). Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Annals of Tourism Research, 102913. https://doi.org/10.1016/j.annals.2020.102913
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