Course syllabus
Course dates: 1 september - 1 October 2025
Course Descriptions
This course in applied epidemiology and biostatistics introduces a broad range of methods used in quantitative research to explore the when, where, who, and why of health and disease distribution. These skills are essential for public health professionals at all levels.
For instance, in infectious disease epidemiology, health authorities and researchers respond to outbreaks by examining: when cases occur (epidemic curve), where the disease is most or least prevalent (geographic distribution), who is affected (populations in vulnerable contexts), and why it follows a certain pattern (determinants). This same framework can be applied to other health challenges, such as rising obesity rates, mental health issues, persistent undernutrition, teenage pregnancies, re-emerging infectious diseases like dengue, and the health impacts of conflicts or natural disasters.
Students will learn the foundational concepts of each method and how they are applied in quantitative research. They will also gain hands-on experience through introductory-level computer sessions and discussions on interpreting and critiquing analyses, including their relevance to public health policies and interventions.
Covering a wide range of epidemiological and biostatistical methods, this course equips students with a solid foundation for further study and professional application in public health research.
Learning outcomes
On successful completion of the course, the student will be able to:
Knowledge and understanding
- describe quantitative research methods to answer the questions of “who, when, where, and why” in the context of studies of the distribution and determinants of health and disease;
- describe the concepts of 'surveillance' and 'time and space' in epidemiology and their application in the context of spatial and temporal patterns of health and disease;
- explain the theoretical frameworks used to understand the determinants of health;
- define the concept of causality, understand different types of causation, explain counterfactual thinking for causal inference and interpret causal diagrams
Competence and skills
- evaluate the quality of available surveillance data and apply methods to analyse and visualise the spatial and temporal data;
- argue for the relevance of the concept of social explanatory factors and apply an intersectional approach to identify patterns, drivers and intervention points to address health and disease;
- apply the causal framework to understand the association between health factors and health outcomes, and analyse causal diagrams;
Judgement and approach
- assess the scientific quality of research articles with a focus on scientific rigour and potential bias based on the formulation of research questions, choice and application of analytical methodology, interpretation of results and practical implications of research findings for policies and interventions;
- reflect on the ethical implications of choosing irrelevant methods, using biased data and misinterpreting research results.
Course information
Contact information
Course leader: Nawi Ng (nawi.ng@gu.se)
Course administrator: Helen Borgkvist (helen.borgkvist@gu.se).
Please include the course code MGH311 in the subject line when you email us.
General student information