Abstract

Agent-based modelling is a versatile approach employed across various fields to simulate and analyse complex systems. In my presentation, I will introduce innovative methods and tools from Software Engineering that I believe have the potential to enhance the work of agent-based modellers in the field of Epidemiology. I will start with an exploration of state machines, focusing on their effectiveness in representing the dynamic states of individual agents and the transitions between these states, which could help in modelling complex epidemiological processes like disease spread and control strategies more accurately. Following this, I will present a novel framework that employs methods from Software Engineering to facilitate the co-creation of agent-based models. This framework aims to bridge the gap between stakeholders and modellers, enabling more collaborative and accurate model development. Finally, I will discuss approaches to optimising resource efficiency in large-scale agent-based models. This will include an examination of multi-method modelling techniques and the use of collective person agents, which can significantly reduce computational costs while maintaining model fidelity.