Specialized 1-week courses

Florence, 6 – 10 July, 2020

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

6 -9 July 2020

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.


Genetic and Epigenetic Epidemiology (5 days course)

6 -10 July 2020

Dr. David Evans, University of Queensland, Australia, Dr. Gibran Hemani, University of Bristol, Bristol, UK, Dr. Rebecca Richmond, University of Bristol, Bristol, UK, and Dr Gemma Sharp, 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


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

6 – 9 July 2020

Dr. Annibale Biggeri, University of Florence, Florence, Italy, and Dr. Toshiro Tango, Center for Medical Statistics, Teikyo University Graduate School of Public Health, Tokio, Japan


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 state-of-the-art methods in disease clustering and disease mapping, a sub-branch of spatial statistics whose focus is on hot spots identification and 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.

Scanning for hot spots of disease cases in time and/or in space is essential part of epidemiological surveillance. In the last two days of the course we shall focus our attention on case studies of disease clustering. We will review relevant literature, highlight potentially misleading approaches and introduce update methodologies.In the first part of the course we will introduce geostatistical methods and we will review popular methods for disease mapping. In low-resource settings, household surveys are a fundamental tool to quantify the disease burden, while In developed countries, disease registries provide detailed information on individuals with a specific disease or condition. Bayesian modeling will be introduced and justified. 

Specific extensions to active surveillance and high risk area profiling will be discussed. This section of the course will show the connections between the two approaches and present the course topics in a unique frame.