# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 4 Discussion  
Modeling the spread of COVID-19 has been a crucial piece in the pandemic response and agent-based models have played an important part of the modeling work done on COVID-19. The model presented in this paper is part of the literature surrounding models that were used during the COVID-19 pandemic. Models have been used to look at the effectiveness of contact tracing apps [15], or the impacts of interventions on ICU beds [14]. However, comparisons between model performance and model output are difficult to make, as there are no other agent-based models that simulate the spread of COVID-19  
Figure 8: Plots showing average number of infectious agents at a given time based on their age across 30 runs of the two scenarios. Figure 7: Plot showing average number of infectious agents at a given time based on their location of infection across 30 runs of the two mixing scenarios. within Ireland, and it is not just the viral dynamics that allow for COVID-19 to spread as it has, but also the interactions of other factors, including the specific interventions a country put into place, the demographic makeup of a country, and the behaviors of the individuals. Additionally, as there is no set way to create an agent-based model for the spread of an infectious disease, parts of our model were designed to answer specific questions. For example, what was the impact of contact tracing or how does childcare by grandparents impact the number of cases in the over 65 population? Different models in the literature have included factors to address other questions. For example, Covasim looks at the health system capacity and thus has agents move from presymptomatic to mild, severe, and critical stages of infection [13]. We do not consider severity within our model but look at behaviors instead (not isolating, isolating, and waiting for a test). The [PERSON] et al. model for France [14] also looks at health capacity and thus includes an agent's likelihood to have certain health conditions that might make COVID-19 more severe, such as obesity or diabetes. These factors are not included in our model, as we were not aiming to look at disease severity or health system capacity. Agent-based models are computationally intensive and in some cases, work needs to be done to reduce the computing power needed to run the models. The scaling factor used in the model presented here reduces the number of agents needed to run the model which in turn decreases the memory needed to run the model. A scaling factor is used in other COVID-19 agent-based models, for example, the Covasim model [13] or the [PERSON] et al. model [16]; however, our scaling factor remains static while the factor used in the other models is dynamic. Although the scaling factor reduces the real-world fidelity of the model, we have shown through our model validation that the scaling of the model does not impact the average results. Thus the results produced by the model can help to better understand the COVID-19 pandemic and the impacts of different interventions. The scaling factor does, however, lead to an increase in the variability in model output. The level of variability as well as how well the scaled model matches the output of an un-scaled model depends on the number of agents infected in the model. This suggests that there are thresholds above which the scaled model performs better. Future work on the scaling factor may involve investigating these thresholds in greater detail. For example, looking at different scaling factors, such as 1 agent to 50 people or 1 agent to 200 people, or alternative scaling methods, such as performing a clustering analysis on a set of individual characteristics, such as age, gender, family size, socioeconomic status, and geographic location, to find clusters of the population that can be represented by a single agent.