Intended learning outcomes (knowledge, skills and competences to be developed by the students):
The objectives of the course are to introduce students to a holistic perspective on current global challenges and computation-based approaches to pathogen transmission, surveillance, control and health. The aim is to prepare students with a better understanding of the methodological and research landscapes of contemporary epidemiology and global health.
Students will develop skills in:
- computation-based approaches to health-related topics
- contemporary epidemiology
- contemporary pathogen surveillance and control
- contemporary challenges related to global health
- contemporary opportunities related to global health
Syllabus:
Lectures
- Theory of contemporary epidemiology
- Practice of contemporary epidemiology
- Pathogen population biology
- Pathogen antigenic diversity
- Pathogen antimicrobial resistance
- Pandemics
- One Health
- Climate and climate change
- Surveillance, Vaccination and Control
Laboratory practice
- Introduction to computation
- Epidemiological modelling I – classic models
- Epidemiological modelling II – classic models
- Epidemiological modelling III – multi-strain models
- Epidemiological modelling IV – vector-borne models
- Phylogenetic modelling
- Machine learning modelling
Laboratory practice
- Individual practice I – classic models
- Individual practice II – multi-strain pathogens
- Individual practice III – vector-borne pathogens
- Individual practice IV – machine learning
Seminars
Topics of focus, including real-world research case-studies:
- Genomic surveillance
- One Health research
- Vector-borne research
- Machine learning research
- Climate and climate change research
- Antimicrobial resistance research
- Vaccination and Control initiatives research