A Comparison of Agent-Based Models and Equation Based Models for Infectious Disease Epidemiology

There are two main methods that are used to model the spread of an infectious disease: agent-based modelling and equation based modelling. In this paper, we compare the results from an example implementation of each method, and show that although the agent-based model takes longer to setup and run, it provides additional information that is not available when using an equation based model. Specifically, the ability of the agent-based model to capture heterogeneous mixing and agent interactions enables it to give a better overall view of an outbreak. We compare the performance of both models by simulating a measles outbreak in 33 different Irish towns and measuring the outcomes of this outbreak

Understanding the assumptions of an SEIR compartmental model using agentization and a complexity hierarchy

Equation-based and agent-based models are popular methods in understanding disease dynamics. Although there are many types of equation-based models, the most common is the SIR compartmental model that assumes homogeneous mixing and populations. One way to understand the effects of these assumptions is by agentization. Equation-based models can be agentized by creating a simple agent-based model that replicates the results of the equationbased model, then by adding complexity to these agentized models it is possible to break the assumptions of homogeneous mixing and populations and test how breaking these assumptions results in different outputs. We report a set of experiments comparing the outputs of an SEIR model and a set of agent-based models of varying levels of complexity, using as a case study a measles outbreak in a town in Ireland. We define and use a six level complexity hierarchy for agent-based models to create a set of progressively more complex variants of an agentized SEIR model for the spread of infectious disease. We then compare the results of the agentbased model at each level of complexity with results of the SEIR model to determine when the agentization breaks. Our analysis shows this occurs on the fourth step of complexity, when scheduled movements are added into the model. When agents networks and behaviours are complex the peak of the outbreak is shifted to the right and is lower than in the SEIR model suggesting that heterogeneous populations and mixing patterns lead to slower outbreaks compared homogeneous populations and mixing patterns

A Hybrid Agent-Based and Equation Based Epidemiological Model for the Spread of Infectious Diseases

Infectious disease models are essential in understanding how an outbreak might occur and how best to mitigate an outbreak. One of the most important factors in modelling a disease is choosing an appropriate model and determining the assump tions needed to create the model. The main research questions this thesis addresses are how do we create a model for the spread of infectious diseases that captures heterogeneous agents without using an inordinate amount of computing power and how can we use that model to plan for future infectious disease outbreaks. We start our work by analysing and comparing equation based and agent based models and determine that an agent-based model’s stochasticity and ability to capture emerging results (complex and hard to explain results from interactions of agents) means that the agent-based model has an advantage in modelling the in dividual actions and complexities that make one infectious disease outbreak differ from another. Focusing on agent-based models, we take the model in two direc tions adding complexity and scaling up the model. Although adding complexity allows us to produce robust results, it increases run time so modelling anything beyond a small population is not feasible. Thus we focus on scaling up the model (from a town to a county) and determining what trade-offs need to be made to keep the model computationally tractable. With our scaled up model we look at characteristics of a town that come from its place in a network of towns, looking at how the centrality of a town affects how an outbreak spreads from a town and enters a town. We determine when a town has a high in degree centrality the i centrality of the other towns are not as important with respect to whether the outbreak will spread to the other towns. The additional agents in the scaled up model lead to an extended run time. In order to reduce run time we make an assumption about the importance of heterogeneous mixing when there is a large number of agents infected and create a hybrid agent-based and equation based model that switches between an agent based disease component and an equation based disease component based on a threshold of the number of agents infected. The hybrid model is able to save time compared to a fully agent-based model without losing a significant level of fidelity. This allows for the model to be scaled up to larger geographies and populations. Scaling the model to larger populations is essential in studying and testing the efficacy of interventions that would not be applicable at a smaller scale. To show this we use the hybrid model to analyse the effects of school closure policies across a network of towns, showing that closing both the town where an outbreak starts in and the town in the region with the highest in degree centrality can help mitigate an outbreak