The SARS-CoV-2 virus, more commonly known as the coronavirus, is arguably responsible for the biggest global crisis in recent history. In an attempt to effectively deal with this crisis, politicians around the globe have been using simulation models in their policy development. There are multiple types of modelling techniques used for epidemiological transmission modelling (Alsharhan, 2021; Anastassopoulou, Russo, Tsakris, & Siettos, 2020). Each different method used has different advantages and disadvantages related to them. One of the used simulation methods for transmission modelling is the Agent-Based Modelling (ABM) technique. The technique is a bottom-up approach, meaning it focusses on the behaviour of individuals to gather knowledge about the resulting emergent overall system behaviour. This technique specifically excels at developing early epidemic growth profiles, however, to gain this feature it needs to process a large amount of data. This data is not always readily available. Even if it is available, the amount of data that needs to be processed combined with the focus on individual behaviour, requires a lot of computational power for simulations making a robust uncertainty analysis very time consuming. An alternative technique is equation-based modelling, with System Dynamics (SD) being an instance of it with additional benefits regarding communication. This is more of a top-down approach, focussing on the overall mechanics of the systems instead of the behaviour of the individuals in the system. Working with aggregate values for most if not all variables to create increased understanding in the system behaviour under different circumstances. Because this technique works with these aggregate values, there is a less of a computational strain when simulating the model. However, this comes at the costs of being able to generate accurate early epidemic growth profiles, as this technique is not capable of fully incorporating key concepts for transmission models, such as heterogeneity of agents, spatial effects, and stochasticity. These two aforementioned modelling techniques, ABM and SD, have characteristic that lean themselves well to cover for each other’s weaknesses. Utilising a technique that dynamically switches between the two modelling methods depending on the state of the model, could incorporate the strengths both models have, this is called dynamic coarse-graining. These strengths are the accuracy and incorporation of key concepts for the ABM side of the model, combined with the simulation speed of the SD side of the model. This dynamic coarse-graining is still in its infancy, as research related to it is very limited. The goal of this research is to examine what dynamically coarse-graining an ABM model to a SD model would mean for the simulation speed of the model, and whether the results will stay consistent with the more accurate ABM method. The current research that has been performed on this topic has been on behaviourally stable models (Bobashev et al., 2007; Gray and Wotherspoon, 2012), meaning there are no changes to the behavioural mode of the model during the dynamic coarse-graining process. In this research two epidemiological transmission models are analysed. The first model will be a relatively simple model that is similarly incapable of exhibiting behavioural changes during the switching process. This Simple model is used as a test-case, for a more extensive SARS-CoV-2 specific model. This Extensive model will include the option of behavioural change during the switching process itself, meaning both the dynamic switching condition and agents’ behaviour is dependent on disease state. As an added benefit, by comparing the results of the two different models, the gained insights are more generalisable...