# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 1 Introduction  
During an infectious disease outbreak, modeling can be an essential tool to help understand how a disease might spread and the possible impact of any interventions [1]. Modeling has been used to respond to the UK foot and mouth epidemic in 2001 [2], the H1N1 pandemic in 2009 [3] and more recently during the COVID-19 pandemic. Infectious disease modeling has shown to be an important part of many government responses. For example, models have been used as evidence for lockdowns in the UK and USA [4], models have also been used in the Irish response to the pandemic [5], and Australia has used models to understand the impacts of lifting restrictions [6]. Equation-based models, in particular, compartmental models, are the most common type of model used for infectious disease modeling. A compartmental model is made up of a set of differential equations. The simplest compartmental model is the SIR model that is made up of three compartments: susceptible (S), infected (I) and recovered (R). Variations of the model can include additional compartments, such as the SEIR model, which includes an exposed (E) compartment [7]. The population in a compartmental model is assumed to be homogeneous and well mixed [8]. While the homogeneous SEIR model is often used and is able to accurately predict infectious disease dynamics, in some scenarios, more detail is needed, and the heterogeneity of the population needs to be taken into account. One common method for this is to add additional compartments to the SEIR model that represent different cohorts of the population. This can be done for age groups [9] or vaccination status [10]. These models can play an important role in understanding how an infectious disease will spread when the heterogeneity of the population is important, such as when vaccinations are implemented by age group. Although a cohort SEIR model allows for heterogeneity in the mixing between compartments, there are some drawbacks. Each compartment is still homogeneous, and the model might not be able to capture the individual actions and variations in characteristics that drive a pandemic [8]. Agent-based models (ABMs) can be used when this greater level of detail is necessary. ABMs are a type of computer simulation made up of agents who interact with each other and their environment [11]. Those agents can be created so that they emulate a real population. This synthetic agent population can match the real population on different characteristics and geographic distribution, and agent networks can produce results due to the interactions between these factors [12]. There are a number of situations where there is an advantage to using ABMs compared to equation-based models. They allow us to create a baseline scenario where we can look at what would have happened if a certain intervention had not occurred, for example, if there was no lockdown, or if vaccinations were not introduced. Additionally, as ABMs are stochastic and are driven by agent decisions, each model run is slightly different, and this stochasticity gives a range of outcomes that could occur within the system. Because of these advantages, a number of ABMs have been developed to help respond to and better understand the COVID-19 pandemic [4; 6; 13; 14; 15; 16]. The model we present in this paper was part of a suite of modeling tools developed by the Irish Epidemiological Modeling Advisory Group (IEMAG). IEMAG was a group formed to provide statistical and mathematical modeling support and advice to the chief medical officer and the national public health emergency team during the COVID-19 pandemic. ABMs, however, can be computationally intensive, which can limit their usefulness when speed of results is necessary [17]. A number of studies have attempted to reduce the computing power while still retaining model fidelity. One method is using a hybrid agent-based and equation-based method designed to save computing power by switching between the two modeling types, depending on the number of infected agents in the simulation [18].