A System Dynamics Approach to Understanding the Covid-19 (Novel Coronavirus) Pandemic

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.