Pre-Course week


  • GIS (Geographic Information Systems) in Epidemiology 
  • Principles of prevention: revisiting population and individual approaches in the “omics” and big data era
  • Modern methods in occupational epidemiology
  • Geo-spatial methods for global health applications


GIS (Geographic Information Systems) in Epidemiology

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

Tue 12 to Fri 15 June 2018 (4 full days), 9am to 5pm.

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.

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

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;
  • be able to perform basic spatial analyses in a GIS.

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


Principles of prevention: revisiting population and individual approaches in the “omics” and big data era

Dr. Rodolfo Saracci, International Agency for Research on cancer (IARC), Lyon, France.

Mon 11 to Fri 15 June 2018 : 4 full days (Monday to Thursday), 9am to 5pm , and one half-day (Friday)


The purpose of this course, addressed to health professionals involved or interested in prevention and its epidemiological foundations, is to revisit and discuss the principles of disease prevention as presented in Geoffrey Rose’s 1992 book ” The strategy of preventive medicine” in the context of today advances in biomedicine and of ‘big data’ accessibility.

The concepts highlighted in the book were based on epidemiological evidence mostly from economically developed countries : they are today increasingly pertinent for low and middle income countries as well, given their raising burden of non-communicable diseases. On the other hand the changes in medical knowledge, technologies, practices and ‘big data’ accessibility characterising the ‘omics’ era open to interrogations – to be developed in the course- the extent and forms in which these concepts and principles may today be applicable.

The course will consist of lectures and sessions involving critical reading of papers, exercise problems and ‘for -against’ debates. Selected passages from Rose’s book will be used as starting points and prompters of some sessions. Number of participants : twenty. 


Modern methods in occupational epidemiology

A training school co-ordinated by COST Action CA16216 “Network on the Coordination and Harmonisation of European Occupational Cohorts”

Dr. John Cherrie,
Institute of Occupational Medicine (IOM), Dr. Manolis Kogevinas, Dr. Neil Pearce, London School of Hygiene and Tropical Medicine, London, UK, Dr. Eva Schernhammer, Medical University of Vienna, and Dr. Michelle Turner, Barcelona Institute for Global Health, Barcelona, Spain.

John Cherrie, Institute of Occupational Medicine (IOM)

Mon 11 to Fry 15 June 2018 (5 full days),
9am to 6pm. (June 15, the course will end at 2pm).


Occupation and paid employment is an essential component of adult life and a major determinant of health and healthy ageing. Several work-related aspects, including working long hours and various chemical/physical exposures, have been associated with cancer, cardiovascular disease, depression and a broad range of other health outcomes. This introductory course on Modern Methods in Occupational Epidemiology, seeks to provide training and networking opportunities for students and early career researchers in occupational epidemiology and exposure assessment. The pre-course will cover topics on: study design in occupational epidemiology, exposure assessment, risk assessment, types of bias including confounding and the use of Directed Acyclic Graphs (DAGs), and current debates on causality.  The format of the course will be based on a series of faculty lectures and student exercises.  Basic knowledge of epidemiology, occupational health, or a related discipline is required.


Training grant application form


Geo-spatial methods for global health applications

Dr. Annibale Biggeri, Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy, and Dr. Emanuele Giorgi, Lancaster Medical School, Lancaster University, Lancaster, UK.

Mon 11 to Thu 14 June 2018 (4 full days),
9am to 6pm.


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.