Epidemiology - courses for professionals

Epidemiology focuses on the occurrence, distribution, and determinants of health and disease, and on the methods used to study health and disease. Epidemiology is considered a cornerstone of public health, preventive medicine, and evidence-based clinical medicine. The courses of the master’s programme in Epidemiology provide students with a solid foundation in the theoretical concepts, methods, and interpretation of epidemiological research with ample attention for the diagnosis, determinants, and prognosis of disease. You will also study the effectiveness of preventive and therapeutic interventions.

It is possible for professionals to follow one or more of the courses of this programme as separate post-academic courses.

Read more about:


Course code and datesTitleTopics
EPI4920 (1.5 ECTS)
Five-day course (Mo-Fr) in the first week of the Academic Year
Introduction to Epidemiology

Introduction to:

  • Epidemiological measures
  • Study designs
  • Concepts of validity, bias and confounding
  • Basic Health measurement
EPI4921 (6 ECTS)
7 Tuesdays in September-October
Written exam on last Tuesday
Observational Research
  • Measures of disease frequency and association
  • Standardization and misclassification
  • Observational study designs
  • Confounding and effect modification
  • Effect modification
  • Causal reasoning and diagrams
EPI4922 (5 ECTS)
7 Fridays in September-October
Written exam on last Friday
Intervention Research in Healthcare
  • Randomized experimental designs
  • Randomization
  • Medical ethical aspects and Patient Information
  • Power and sample size
  • Handling of missing values
EPI4923 (6 ECTS)
8 Tuesdays in November-December
Written exam on last Tuesday
Advanced Statistical Analysis
  • Analysis of variance and covariance
  • Linear regression analysis
  • Repeated measurements analysis
  • Logistic regression analysis
  • Survival analysis
  • Choice of statistical analysis
EPI4924 (5 ECTS)
8 Fridays in November-December
Written exam on last Friday
  • General principles of measurement
  • Development of a new measurement scale
  • Validity, reliability and responsiveness
  • Diagnostic testing and types of bias
  • Measurement error and sources of variation
EPI4931 (3 ECTS)
4 Tuesdays in January
  • Systematic literature reviews
  • Literature search
  • Heterogeneity of study results
  • Fixed versus random effects
  • Small study bias and publication bias
  • Introduction to R
EPI4925 (3 ECTS)
4 Fridays in January
Molecular and Genetic Epidemiology
  • Biomarkers
  • Study designs in molecular epidemiology
  • Genetic epidemiology
  • Mendelian randomization
  • Big data / omics
EPI4934A (3 ECTS)
3 Fridays in February-March
Handing in assignment on 4th friday
Applied epidemiology of infectious diseases
  • Concepts in infectious diseases epidemiology
  • Surveillance
  • Outbreak management
  • Prevention of infectious diseases
  • Role of the Public Health Service
EPI4934B (3 ECTS)
3 Fridays in February-March
Handing in assignment on 4th friday
Clinical Data Science
  • Overlap between epidemiology and data science
  • Data exploration and visualization
  • Data simplification and dimensionality reduction
  • Creating understandable code (basic knowledge of R required)
EPI4934C (3 ECTS)
3 Fridays in March
Handing in assignment on 4th friday
Health Technology Assessment/ Health Economic Evaluation

Basics of economic evaluations (EE):

  • Need for EE
  • EE design, identification, measurement & valuation of outcomes and costs
  • Synthesizing cost and effects, reporting, critical appraisal, uncertainty, modelling and trial-based approaches
EPI4934D (3 ECTS)
3 Fridays in March
Handing in assignment on 4th friday
Clinical Prediction Models
  • Statistical methods
  • Machine-learning methods
  • Developing prediction models
  • Validation of prediction models
  • Impact of prediction models (basic knowledge of R required)


All courses are four or eight week-part time courses on either Tuesdays or Fridays. Week 4 or week 8 is the exam week (or the deadline for handing in written assignment(s)). Students will gain up-to-date theoretical epidemiological knowledge, while there is also a strong emphasis on the practical training of students to equip them with skills and competences relevant for epidemiological research.

What others say about the courses

daniek meijs

"As a PhD student, I felt I lacked basic knowledge related to epidemiology and statistics. These subjects came up daily during my work, but were underexposed in my studies. To brush up my knowledge, I chose to take three separate courses of the master Epidemiology in Maastricht. In these courses, the basic principles were repeated and deepened through lectures, teaching groups and practicals. The learning effect was enormous. I apply the knowledge I acquired on a daily basis and try to pass it on to new students. Highly recommended to fellow PhD students and researchers!”

Daniek Meijs MSc Medicine, PhD student at the department of Intensive Care at Maastricht UMC+



Participants in this course are PhD students or medical doctors, and graduates from medical or health sciences studies from universities or universities of applied sciences who wish to enhance their knowledge about epidemiology and statistics. 

Teaching methods and staff

Maastricht University uses the Problem-Based Learning method. You will gain knowledge on the courses through group assignments, workshops, lectures and skills training sessions. You will work in small groups of 10-12 participants.

The high qualified and experienced teaching staff consists of experts on (molecular) epidemiology, statistics, health technology assessment, data science, prediction modelling, and infectious diseases. Prof.dr.ir. Matty Weijenberg, dr. Sander van Kuijk, and dr. Colinda Simons are among the staff members.

Admission requirements

  • Bachelor’s degree in Health Sciences, Biomedical Sciences or Medicine from a university of a university of applied sciences, or an equivalent. 
  • Basic statistical knowledge is expected, and students are expected to be familiar with statistical programs like SPSS or R (courses EPI4932B and EPI4932D)
  • Sufficient English skills (language of instruction is in English).
  • Admission requirements for following individual courses of this master programme are relatively lenient. Should a participant at some point decide to follow the entire master programme and obtain a master’s degree, the regular admission requirements apply and a thesis has to be written. 
  • You can register by sending an email to examensfhml@maastrichtuniversity.nl indicating in which course or courses you are interested and including a short motivational letter. You will receive a registration form and invoice by return.


Upon completion of the course, participants will receive a certificate from Maastricht University.

Course fee

In the academic year 2023-2024 the fees are:
1.5 ECTS    €477
3 ECTS        €954
5 ECTS        €1590
6 ECTS        €1908


Maastricht University
Universiteitssingel 40, 50 and 60 
6229-ER Maastricht