A Tutorial on How to Set Up a System Dynamics Simulation on the Example of Covid-19 Pandemic

The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling, which incorporates systems thinking to understand and map complex events as well as correlations, can aid in predicting future outcomes of the pandemic and generate key learnings. As system dynamic modeling allows for a deeper understanding of the manifestation and dynamics of disease, it was helpful when examining the implications of the pandemic on the supply chain of semiconductor companies.

This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling.

The first step is to construct the initial structure – define major stocks and flows, seen as the model’s backbone or skeleton. The stocks are always calculated as people, and the flows are in people per day. The susceptible stock is overall affected by three feedback loops: vaccination, immunization, and reinfection, representing the inflows and outflows of the susceptible stock.

Vaccinations are necessary to add to the model due to the way they altered the course of the pandemic. The action is implemented through the stock “Immunized,” representing the number of people immunized by the vaccines. This is affected by vaccine efficacy and the number of vaccines administered per day, among other parameters.

As the pandemic evolved, the cases of reinfection increased, which introduced the risk of stocks previously seen as immunized and recovered to be reinfected. The rate at which this happens is based on the reinfection fraction and the size of the stocks getting reinfected. The model, therefore, adds a flow back to the susceptible stock from the recovered undetected, the recovered hospitalized, and the vaccinated. This creates reinforcing loops, increasing the amount of susceptible and furthering the amount of infectious and recovered.

As a way to incorporate the effect that general governmental measures have on the spread of Covid-19 the model includes the exposed stock. This stock refers to the number of people who have been infected but exhibit no symptoms or clinical signs and are still in the incubation period, meaning they test negative but still risk infecting people. The size of this stock is mainly related to the contact rate, governmental measures, and several mutant variations. The greatest impacts for this part of the simulation are the transition times, meaning how long people remain in certain compartments, the death rates, detection rates, hidden factors, and disease-specific characteristics. For the infectious stock following the three different branches, this is implemented to mimic the issue with undetected infections and the possibility to quarantine when notably infected.

After the construction of the model, it is essential to acknowledge its front-end processes. It displays and allows for simplified alteration of parameters to adapt to current changes and developments in the simulation in a user-friendly manner. This is created through AL’s Experiment function, which offers the possibility to conduct scenario analysis using built-in experiments such as Sensitivity Analysis experiments, Compare Run experiments, and Parameter Variation experiments. These built-in experiments are at the disposal of decision-makers to further analyze the epidemiological situation.

For a more in-depth understanding of this function, please refer to AnyLogic Experiments. After fabrication of the model, a simulation can be made, and results can be acquired.

The model showing the parameters, including the infectious, infected, recovered, and deceased stocks
The model part specifically including the infectious, infected, recovered, and deceased stocks and linked parameters

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