Summary
Epidemiological modelling can be a powerful tool to assist animal health policy development and disease prevention and control. Models can vary from simple deterministic mathematical models through to complex spatially-explicit stochastic simulations and decision support systems. The approach used will vary depending on the purpose of the study, how well the epidemiology of a disease is understood, the amount and quality of data available, and the background and experience of the modellers. Epidemiological models can be classified into various categories depending on their treatment of variability, chance and uncertainty (deterministic or stochastic), time (continuous or discrete intervals), space (non-spatial or spatial) and the structure of the population (homogenous or heterogeneous mixing). The increasing sophistication of computers, together with greater recognition of the importance of spatial elements in the spread and control of disease, mean that models which incorporate spatial components are becoming more important in epidemiological studies. Multidisciplinary approaches using a range of new technologies make it possible to build more sophisticated models of animal disease. New generation epidemiological models enable disease to be studied in the context of physical, economic, technological, health, media and political infrastructures. To be useful in policy development, models must be fit for purpose and appropriately verified and validated. This involves ensuring that the model is an adequate representation of the system under study and that its outputs are sufficiently accurate and precise for the intended purpose. Finally, models are just one tool for providing technical advice, and should not be considered in isolation from data from experimental and field studies.
 
Keywords
Disease – Epidemiology – Model – Policy – Principles – Simulation.