Unit | Biostatistics

The Biostatistics unit engages in collaborative, basic and applied statistical research in the fields of epidemiology, parasitology and infection biology. The unit conducts theoretical and applied research on Bayesian spatio-temporal disease modelling (closely interwoven with the Ecosystem Health Sciences unit), and engages in research on modelling cohort data from Switzerland and Europe (in close collaboration with the Chronic Disease Epidemiology and Environmental Exposures and Health units).

Major areas of methodological research

  • Spatio-temporal modelling for disease burden estimation and surveillance
  • Diagnostic error evaluation
  • Cohort data modelling
  • Exposure modelling
  • Causal inference
  • Meta analysis
  • Bayesian computation

Services

The unit leads Swiss TPH's scientific support services. This service is provided in collaboration with the Public Health Computing group and includes consulting for study design, data management support, statistical analysis, consulting in the fields of biomathematics and bioinformatics, and software development. Clients come from within Swiss TPH and externally.

Data Services

Teaching

The unit is also heavily engaged in teaching Statistics and Epidemiology to medical undergraduates, MSc students and PhD students, both in curricular courses of the University of Basel and in external courses. The Unit is involved in Swiss Master of Public Health Programme, European Course in Tropical Epidemiology and Postgraduate Programme for University Professionals in Insurance Medicine.

Statistics and epidemiology teaching is provided within the programs of the University of Basel. Within this program we organise the following courses:

We also teach on the following courses:

  • Health Care and Management in Tropical Countries (HCMTC) This is an annual three-month diploma course accredited by TropEd for the degree of Master of International Health.
  • Bayesian Disease Mapping in Epidemiology and Public Health, a course for researchers dealing with spatial data in epidemiology and public health.