Postgraduate Certificate in Risk Analysis in Health and Food Safety

Postgraduate Certificate in Risk Analysis in Health and Food Safety

The Royal Veterinary College and EpiX Analytics

(Now open for registration)

Dear Colleagues, 

We are pleased to announce that The Royal Veterinary College and EpiX Analytics are now offering a Postgraduate Certificate in Risk Analysis in Health and Food Safety, starting in March 2013. This programme is among the first recognized qualifications in Risk Analysis in Health and Food Safety and its successful completion will lead to the award of a Postgraduate Certificate by the University of London. 

The program aims at providing the skills for cutting-edge and in-depth practice in quantitative risk analysis in health and food safety and builds on previously successful training programs delivered by the Veterinary Epidemiology & Public Health Group of the Royal Veterinary College (FAO Reference Centre in Veterinary Epidemiology) and EpiX Analytics, a leading risk analysis consultancy. 

The Postgraduate Certificate is designed to accommodate full-time working professionals and graduate students and offers candidates the opportunity to develop hands-on work skills and tailor their studies to their own professional and development needs. 

For further information, please visit: www.rvc.ac.uk/rahfs or www.epixanalytics.com/RAHFS.html or contact Barbara O’Neill at: BOneill@epixanalytics.com

 

Advanced Epi Course in Copenhagen

Dear All,

I would like to announce the PhD course:

Epidemiological analysis of clustered data - with emphasis on veterinary and animal science applications

Which is being held at University of Copenhagen, January 7-11, 2013.

This course is designed to enable the participant to:

1) Determine if clustering is present in their data.

2) Understand why clustering might be a problem and understand its consequences on the analysis of either continuous or discrete data.

3) Understand the uses, advantages and limitations of simpler methods to deal with clustering.

4) Use the statistical software R to characterize and analyse clustered data with a hierarchical and/or longitudinal structure.

5) Summarize and present the results and conclusions in the format of a scientific paper

This is an advanced course in veterinary epidemiology. The course covers the clustering which most often arises in veterinary and animal science due to the structure of animals being housed together on farms (possibly within pens, within sections). The focus is on understanding the consequences first, and secondly being able to analyse data and present results while addressing the clustering, particularly through the use of linear mixed models and generalized linear mixed models in R.

The course uses the textbook:

Dohoo, Martin & Stryhn (2009) Veterinary Epidemiologic Research, 2nd Edition.

and on-line resources and notes in R.

The course is constructed in three modules:

1) 2-4 January 2013: Warm Up days - Prior to the campus week, the participant have the possibility to refresh the necessary skills in R (while revising the basic concepts of simpler analysis, i.e. logistic regression and analysis of variance). This is done using UCPH’s eLearning environment Absalon

2) 7-11 January 2013: Campus week - A mixture of lectures, discussion and exercises. All days from 9-16.

3) 14 January - 8 February 2013: Project work - The participant must do an analysis (preferably on their own data) and present the results outlined as a scientific paper. This work is done at home - individual supervision is provided on-face or through SKYPE.

The course is free for PhD students under the Danish Open Market, but for other there is a course fee.

For registration and further information:

https://phdkursus.sund.ku.dk/frontPlanner/DetailKursus.aspx?id=96297

The deadline for application is December 17^th, 2012.

/Nils

Nils Toft

Professor

Department of Large Animal Sciences

Department of Large Animal Sciences

Populationbiology

University of Copenhagen

Groennegaardsvej 8

DK - 1870 Frederiksberg C

Denmark

DIR +45 353 33009

nt@sund.ku.dk