- To know how to analyze epidememiological data sets of structured (animal)
populations, which include the application and interpretation of methods to
deal with clustered data.
- To be able to distinguish between risk, probabilities and disease causation.
- To be able apply the correct study design to animal health problems.
- To be able to design sampling schemes to estimate incidence and prevalence
at the right epidemiological unit (e.g. animal, herd, country).
- To be able to determine diagnostic test characteristics for tests with and
without gold standard.
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Period (from-till): This course is taught several times per year, during fulltime weekdays according to the following Schedule (Face-2-Face in Period A, Online in Period B):
Education form |
Startdate |
Enddate |
Registration period |
Face-2-Face |
25-03-2024 |
30-03-2024 |
BMS_P3_A |
Onilne* |
08-04-2024 |
26-04-2024 |
BMS_P3_B |
*Online courses are only available for Epidemiology students following a full online programme.
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Single courses — MSc Epidemiology (msc-epidemiology.nl)
Course coordinator:
Egil Fischer
Course description:
In this course you will learn the "state-of-the-art" in veterinary epidemiology. the course will focus on those aspects that are specific for epidemiology in animal health, but these concepts are relevant also in many other situations.
For instance, structured polulations (e.g. individuals grouped by herd, farm or pen) are common in animal health epidemiology, but also not uncommon in human epidemiology (e.g. nursing homes). You will learn to apply epidemiological and statistical methods, specific for structured populations during hands-on computer practical and lectures.
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
Introduction to Epidemiology
Modern Methods in Data Analysis
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