# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 2 Materials and Methods
### Model Description
#### 2.1.3 Disease  
The disease component in the model is set to simulate the dynamics of COVID-19 and follows the dynamics of an SEIR model with the agents moving from susceptible to exposed then infected and finally recovered. In addition to the four stages of infection, we match the disease component of the model to the Irish population-based SEIR model [5]. In the model, individuals start in a susceptible component, then if infected, they move to an exposed component, and from the exposed component, individuals will move to one of two infectious components: asymptomatic or presymptomatic. If the individual is presymptomatic, they will then move to one of the following components: isolating, not isolated, waiting for a test, and tested. Once recovered, individuals will move to a recovered state. Thus in our model, an infectious agent can be in one of the following states: asymptomatic, presymptomatic, isolating, not isolated, waiting for a test and tested. When a susceptible agent comes into contact with an infectious agent, they have a chance of becoming exposed. Once exposed, the agent will stay exposed for a predetermined period of time before becoming infectious. This predetermined period of time is different for each agent. When an agent is exposed, the agent is assigned a length of time for their exposure period, using an exponential distribution with a mean of the exposure period taken from the literature. Agents will remain infectious for a predetermined period of time before recovering. Similar to the exposure period, this predetermined period of time is different for each agent. When an agent becomes infectious, the agent is assigned a length of time for their infectious period using an exponential distribution with a mean of the infectious period taken from the literature. Once recovered, the agents can no longer be infectious and cannot be re-infected. This assumption was made, as the risk of reinfection early in the pandemic was low [28], and the model was initially designed to look at short term outcomes of the pandemic (i.e., during a single wave). When an agent becomes infectious, the agent will either become asymptomatic or symptomatic. If they are symptomatic, they can either be isolating, not isolating, or waiting for a test. If waiting for a test, the agent will be waiting for a test for a predetermined time and then have tested positive. The infectious component that an agent is in (asymptomatic, isolating, etc.) determines how infectious an agent is. The base infectious rate is determined using the basic reproduction number, \(R_{0}\), the average contacts an agent has, and the infection period [29]. The method for calculating the infectiousness of the agents and all the parameter values are discussed more in the model ODD [26]. Those who are presymptomatic, asymptomatic, and isolated have a reduction in their infectiousness. We implement these reductions to match with those in the SEIR model. The main disease parameters needed to initialize the model are the basic reproduction number, \(R_{0}\), the length of the exposed period, the length of the pre-symptomatic period, and the length of the infectious period, and are taken from the literature.  
Additionally, as there have been multiple variants of COVID-19 during the course of the pandemic, the model allows for agents to pass on specific variants. For example, if an agent is infected with the alpha variant, any agents they infect will also be infected with the alpha variant. As the variants that are widely circulated in the population tend to be more infectious, we also include a variant multiplier that adjusts the infectiousness of an agent for a specific variant.