Simulation models are a valuable tool for understanding the epidemiology of infectious animal diseases and for comparing the cost-effectiveness of control strategies that are too expensive, impractical, or unethical to test in the real world. This guide walks you through the process of building and using simulation models, from describing host population structure and contact patterns, to developing compartmental models that capture key features of disease dynamics, and evaluating interventions at the animal, farm, and industry levels. Through worked examples, you will develop an understanding of the core principles underlying simulation modelling and how model assumptions shape interpretation and decision-making.
Every infectious disease and every host population is different so it is impossible for us to cover every single scenario you will encounter as future infectious disease modellers. This guide is instead designed to provide you with foundations in the process of building models so that you can adapt basic model frameworks to fit your unique epidemiological situation. By the end of these materials, learners will be able to:
The target audience for this guide is epidemiologists, researchers, veterinarians, and postgraduate students with an interest in gaining hands-on experience developing simulation models from start-to-finish. The key concepts are presented from a biologically relevant perspective before delving into the underlying mathematics, which makes the material easily accessible for students with no strong love for equations or programming.
Simulation models are widely used in animal health to compare disease control options when real-world experimentation is impractical or impossible. This module introduces core infectious disease principles, explains how models are planned and scoped, and shows how literature evidence is synthesised into a coherent modelling framework to support veterinary and policy decision-making.
This module focuses on how disease transmission and pathogenesis are translated into compartmental models at the individual animal level. It explores density- and frequency-dependent transmission, introduces deterministic and stochastic modelling approaches, and demonstrates how biological assumptions shape model behaviour and interpretation.
Building on individual-level processes, this module examines how infection dynamics operate within herds. It introduces methods for estimating the reproduction number (R₀), incorporates seasonal and discrete demographic events, and shows how herd structure, management practices, and population turnover influence disease persistence and control.
At larger scales, disease spread is shaped by movements, geography, and contact structure between herds. This module introduces metapopulation models, contact networks, and spatial transmission processes, highlighting how heterogeneous connectivity alters regional and industry-level disease dynamics.
A key strength of simulation modelling is the ability to compare control strategies before they are implemented. This module explores how interventions such as vaccination, testing, culling, and movement restrictions are represented in models, and how economic and production impacts are incorporated to support practical decision-making.
No infectious disease model is free from uncertainty. This module addresses how model parameters are estimated, how sensitivity analyses are used to explore uncertainty, and how assumptions and limitations should be communicated when interpreting model outputs for research, policy, and practice.
Advancing Animal Care through Shared Learning