A Guide to Good Practice for

Quantitative Veterinary Epidemiology

Developing good practice by providing a methodology to ensure that the

processes and procedures used to gather and interpret data are appropriate,

rigorous, repeatable and auditable. 

There are no generally accepted standards for ‘good practice’ in quantitative veterinary epidemiology. This document addresses a variety of issues to do with data collection, ways of analysing and modelling data and communication of the results of such analyses. These issues are relevant in a wide variety of contexts, from ongoing attempts to analyse the impact of badger culling on the incidence of bovine tuberculosis through to the use of mathematical models to inform policy during the epidemics of foot-and-mouth disease in 2001 or of ‘mad cow’ disease (bovine spongiform encephalopathy) in the 1990s. Further, the issues raised and the good practice proposed is relevant beyond the disciplines making up quantitative veterinary epidemiology and certainly apply more broadly to other branches of epidemiology and data driven science.  An underlying aim here is to move away from the current position, where quantitative methods are all too often viewed by policy makers and other stakeholders as a mysterious ‘black box’, to their becoming familiar and valued tools.

These guidelines are written by practitioners for practitioners, whether they are data managers, statisticians, risk analysts, simulation modellers, or scientists advising policy makers. They are not written explicitly for the users of the work undertaken and, although users may have an interest in the contents (if only to see the view from the other side of the fence), the language is unashamedly technical where appropriate.

Do feel free to comment what is presented, using the comments form - this is a 'living document' and valuable comments received will be used to improve future iterations of the Guide.