Module 1, a “Network Analysis” course, was held June 20–22, 2012. Course instructors were Caroline Dubé, DMV MS, PhD, Veterinary Epidemiologist and Scientific Advisor, Animal Health Risk Assessment Unit, Canadian Food Inspection Agency, Montreal, Canada, and Caryl Lockhart, DMV, MSc, PhD, FAO/GLEWS, Veterinary Epidemiologist (Spatial and Network analyses) Animal Production and Health Division, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. Local assistance was provided by Javier Sanchez, DVM, PhD, CFIA Research Chair – Risk Analysis, Canadian Regulatory Veterinary Epidemiology Network, Atlantic Veterinary College.

The 3 day course was designed to provide participants with a practical guide to conducting basic of Social Network analyses in Veterinary Medicine using UCINET. The goal of the course was to have participants develop a set of skills to design/conduct Network analyses studies, and to manipulate and describe the data gathered. Participants were provided with basic concepts of Network theory, examples of networks and descriptive measures of networks and interpretation.

The course focused on the practical aspects of network data management, and network data visualization using UCINET, and R.

Module 2, a 4 day course entitled “Diagnostic Test Evaluation, with Emphasis on Latent Class Analysis”, was instructed by Nils Toft, MSc, PhD, Professor in Quantitative Epidemiological Decision Support, Department of Large Animal Sciences, University of Copenhagen, from June 25–28, 2012.

The course covered the epidemiological aspects of diagnostic test evaluation studies – from the technical aspects of planning, conducting and subsequently analyzing data from such studies, to the underlying assumptions regarding disease definitions, test characteristics and the implications of using a perfect reference tests vs. a latent class approach. The design and analysis of latent class models for test evaluation without a perfect reference test (i.e. a ‘gold’ standard) was the main focus of the course. The participants were introduced to Latent Class Analysis (LCA) in a Bayesian framework using OpenBUGS. Through a mixture of lectures/discussion of the theory and biological implications and exercises/tutorial based on published examples, the course participants were exposed to the necessary concepts and ideas of LCA, and gained working skills in using OpenBUGS and the presented examples to explore the possibilities of diagnostic test evaluation using LCA methods. The course presented models at population and individual based levels, including models that include covariates affecting the test characteristics. Also models where the primary purpose is not test evaluation were presented and discussed.