Specialized 1-week courses

GIS (Geographical Information Systems) in Environmental Epidemiology (4 days course) 

4 -7 July 2022

Dr. Danielle Vienneau and Dr. Kees de Hoogh, Department of Epidemiology and Public Health, SwissTPH, University of Basel, Switzerland


The physical and social environment that surrounds us plays an important part in our health and wellbeing. The geography concept of ‘place’ thus cannot be ignored in environmental epidemiology and public health. Whether investigating the level of environmental pollution, access to recreation or health services, or patterns of disease, Geographic Information Systems (GIS) provide the standard platform for exploring spatial attributes and relationships between our environment and health.

This course offers an introduction to GIS and how it is used in environmental epidemiological research. It will introduce students to the basics including: working with and integrating spatial and non-spatial data; geographic scale and spatial precision; geocoding; visualisation; thematic mapping; and understanding spatial relationships. Specific skills and tools will also be introduced in relation to methods for spatial linkage of exposure, contextual and confounder information for epidemiological or health risk assessment studies.

This course will be a mix of lectures, demonstrations and practical time for hands-on data analysis in ArcGIS and QGIS.

No prior knowledge of GIS is required for this intensive course.

Students will gain knowledge in the fundamentals of GIS for spatial data handling and analysis. By the end of the course, students will

  • Understand how GIS can be used to enhance public health and epidemiological research;
  • Be able to acquire, add, manipulate, visualise and map spatial data in a GIS; and
  • Be able to perform basic spatial analyses in a GIS.


Geo-spatial methods for global health applications with focus on Disease Clustering (4 days course)

4 – 7 July 2022

Dr. Annibale Biggeri, University of Florence, Florence, Italy, and Dr. Emanuele Giorgi, Lancaster Medical School, Lancaster University, Lancaster, UK


The ultimate goal of global health science is to improve health conditions for all people worldwide. In an increasingly interconnected world, tackling the emergence of disease outbreaks requires solutions that transcend national borders. To this end, understanding the spatial variation in disease risk and the exposure to environmental hazards has become increasingly important.

In this course, we introduce start-of-the-art methods in disease mapping, a sub-branch of spatial statistics whose focus is on the prediction of health outcomes and exposures within a geographical area of interest. These methods have found application in public health problems both in developing and developed countries.

In low-resource settings, disease registries are geographically incomplete or non-existent and, therefore, household surveys are a fundamental tool to quantify the disease burden. In the first two days of the course, we shall focus our attention on case studies of tropical disease epidemiology in Africa. More specifically, we will introduce geostatistical methods and show how this can be used to identify disease hotstops, i.e. areas where the disease risk reaches levels that may represent a major public threat.

In developed countries, disease registries provide detailed information on individuals with a specific disease or condition. However, in order to protect confidential information, data are only available at spatially coarser scale than the location of residence. In the second part of the course, popular approaches to disease mapping from areal data will be reviewed. Bayesian modeling will be introduced and justified. Specific extensions to active surveillance and high risk area profiling will be discussed.

Pre-requisites: All participants should have good knowledge of probability, generalized linear regression and likelihood-based inference. The course will use packages in the R software environment. On request, a tutorial on the basics of R can be provided. All lectures and lab sessions are delivered in English.


Modern time series methods for public health and epidemiology (5 days course)

4 – 8 July 2022

Dr. Antonio Gasparrini, London School of Hygiene & Tropical Medicine, London, UK, Dr. Ana Maria Vicedo-Cabrera, University of Bern, Bern, Switzerland, and Dr. Francesco Sera, University of Florence, Florence, Italy


Time series analysis is a key but underused tool for epidemiological and public health research. In the last two decades, there has been an intense activity to develop more sophisticated study designs and statistical models for using time series data in health studies, with applications spanning various research areas. For instance, time series methods can now be applied for evaluating public health interventions, for assessing health effects associated with environmental stressors and climate change, and for quantifying beneficial or side effects of drugs or clinical practices.

This course will offer a thorough overview of established approaches and recent advancements in methods using time series data for health research, including a theoretical introduction as well as practical examples in public health, environmental, clinical, cancer, and pharmaco-epidemiology. The sessions will cover standard time series designs for aggregated data, including multi-location studies and recent extensions for small-area and individual-level analysis. Case studies will illustrate the use of novel data resources such as remote sensing satellite measurements, electronic health records, real-time smartphone data, and climate models for health impact projection studies

The course will involve short lectures followed by practical sessions using the statistical software R, to illustrate the use of time series analysis in various settings using real-data examples. While no previous knowledge on time series methods is expected, having basic experience on the use of R for epidemiological analysis is an advantage


Genetic and Epigenetic Epidemiology (5 days course)

4 – 8 July 2022

Dr. David Evans, University of Queensland, Australia, Dr. Gibran Hemani, University of Bristol, Bristol, UK, Dr. Matthew Suderman, University of Bristol, Bristol, UK, and Dr Paul Yosefi, University of Bristol, Bristol, UK


Genetic epidemiology refers to the study of the role of genetic factors in determining health and disease in families and in populations. Genetic epidemiological studies have made substantial contributions to understanding the aetiology of complex traits and diseases, and hold great promise for personalised healthcare in the future. This course provides an introduction to the design, analysis and interpretation of genetic and epigenetic epidemiological studies of disease, with a focus on genome-wide and epigenome-wide association studies (GWAS and EWAS). Topics that will be covered include design and analysis of GWAS, imputation, meta-analysis, bioinformatic follow-up, whole genome and polygenic approaches including G-REML and LD score regression, epigenetics, EWAS, and Mendelian randomization (MR). As well as lectures, participants will gain practical experience in analysing genetic and epigenetic datasets. We will use the R statistical software package for the majority of analyses and participants will get plenty of hands on training in this package. By the end of the course participants should have a good working knowledge of concepts in genetic and epigenetic epidemiology, and will be able to perform analyses of genetic and epigenetic datasets