Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks.

Both agent-based models and equation-based models can be used to model the spread of an infectious disease. Equation-based models have been shown to capture the overall dynamics of a disease outbreak while agent-based models are able to capture heterogeneous characteristics of agents that drive the spread of an outbreak. However, agent-based models are computationally intensive. To capture the advantages of both the equation-based and agent-based models, we create a hybrid model where the disease component of the hybrid model switches between agent-based and equation-based. The switch is determined using the number of agents infected. We first test the model at the town level and then the county level investigating di erent switch values and geographic levels of switching. We find that a hybrid model is able to save time compared to a fully agent-based model without losing a significant amount of fidelity.

Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays.

Mpox is a viral disease caused by the monkey pox virus (MPXV) of the Poxviridae family. It is similar to smallpox and cowpox. Mpox is not categorized as a sexually transmitted infection, however, most transmission in the recent global outbreak has been attributed to be through men who have sex with men (MSM). Agentbased modelling (ABM) is an effective approach to simulate the spread of Mpox because it enables the detailed representation of heterogeneity among individuals or sub-populations, including variations in behaviour, susceptibility to disease, and contact patterns. This is crucial for accurately modelling diseases in populations with long tailed distributions of characteristics such as sexual activity, that has been seen in the recent Mpox outbreak. Two ABMs are presented in this paper which simulate the spread of Mpox in an artificial population. The parameters for contact formation and dissolution are drawn from the NATSAL-3 dataset (National Survey of Sexual Attitudes and Lifestyles) (Mercer et al. 2013). The presented ABMs are based on the principles of the classic compartmental Susceptible-Exposed-Infected-Recovered (SEIR) model and complexity is further added to break the assumptions of homogeneous mixing and populations. The first ABM is based on an heterogeneous population, consisting of people from two self-identified genders (male and female) and three sexual preferences: homosexual, bisexual and heterosexual. The second ABM is a subset of the first ABM, that focuses on the MSM population, which is considered to be at high-risk of Mpox. We report a set of experiments modelling relevant scenarios by number of imported cases, number of super-spreader events, and different vaccination strategies. Our analysis concludes that Mpox infection reaches locally stable disease-free equilibrium in the absence of concurrent partnerships. Our simulations demonstrate that prioritizing vaccines for the MSM population can significantly reduce the effective R value from 1.62 to 0.19, highlighting the potential impact of targeted vaccination strategies in mitigating Mpox transmission.