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.Īfter the construction of the model, it is essential to acknowledge its front-end processes. 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. The size of this stock is mainly related to the contact rate, governmental measures, and several mutant variations. 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. This creates reinforcing loops, increasing the amount of susceptible and furthering the amount of infectious and recovered.Īs a way to incorporate the effect that general governmental measures have on the spread of Covid-19 the model includes the exposed stock. The model, therefore, adds a flow back to the susceptible stock from the recovered undetected, the recovered hospitalized, and the vaccinated. The rate at which this happens is based on the reinfection fraction and the size of the stocks getting reinfected. This is affected by vaccine efficacy and the number of vaccines administered per day, among other parameters.Īs the pandemic evolved, the cases of reinfection increased, which introduced the risk of stocks previously seen as immunized and recovered to be reinfected. The action is implemented through the stock “Immunized,” representing the number of people immunized by the vaccines. Vaccinations are necessary to add to the model due to the way they altered the course of the pandemic. The susceptible stock is overall affected by three feedback loops: vaccination, immunization, and reinfection, representing the inflows and outflows of the susceptible stock. The stocks are always calculated as people, and the flows are in people per day. The first step is to construct the initial structure – define major stocks and flows, seen as the model’s backbone or skeleton. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. 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. 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. The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world.
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