Universität Bielefeld

© Universität Bielefeld

International Summer School 2016 - Spatial Epidemiology, Climate and Health: Concepts and Modelling

September 26-30, 2016

University of Bielefeld, School of Public Health,
Department of Public Health Medicine

In 2016, this International Summer School aiming at the interface of spatial epidemiology, climate and health now takes place for the 8th time. It is an advanced training course of the Institute for Innovation Transfer at the University of Bielefeld GmbH (IIT) in cooperation with the Faculty of Health Sciences, University of Bielefeld and the Department of Geography, Humboldt-Universität zu Berlin.



The course addresses spatial-epidemiological approaches in the light of global climate change.
We focus on state-of-the-art statistical and spatial statistical modelling to health outcomes and associations with socio-ecological factors in the developing world.
We combine theoretical and lab work on statistical analysis and spatial-epidemiological modelling techniques in a trans-disciplinary approach.
Participants will be working with the statistical software R (http://www.r-project.org). Basic knowledge on R is recommended, but not an exclusion criterion. For preparation you may consider taking a free online course to learn R. For example: https:www.coursera.org/learn/r-programming
Furthermore, there will be a two-hour introductory course into GIS prior to the spatial statistical lectures with QuantumGIS (http://qgis.org).


Learning objectives:

After completing the course, our participants will be able to:

  • Discuss (spatial) epidemiology of health outcomes in relation to a changing climate
  • Review concepts of population projection, epidemiological and demographical transitions
  • Analyse health outcomes in low-income countries by applying
    • Statistical techniques (i.e. multivariate regression analysis)
    • Spatial statistical techniques (i.e. autocorrelation analysis, disease and exposure mapping)
    • Spatial-epidemiological modelling techniques (multivariable regression models that control for spatial dependencies in the data)
  • Work more effectively in collaboration with other disciplines for investigating multidisciplinary problems to develop sustainable strategies for the improvement of living conditions in developing countries.

For further information you can download the Programme




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