Structure and contents

The Master in Health, Major in Digital Public Health, consists of 4 semesters (120 ECTS) and it is offered by USI's Faculty of Biomedical Sciences.

1° Semester (Autumn)

Course Lecturer ECTS
Health communication P. Schulz 6
Introduction to research approaches in health  S. Suggs 6
Fundamentals of global and public health E. Albanese 6
Digital health A. Camerini 3
Project management P. Goncalves 3
Health economics and policy F. Mazzonna / G. Masiero  6
Total 1° semester  

30

2° Semester (Spring)

Course Lecturer ECTS
Public health in the digital era E. Albanese 6
Industry and institutions perspectives: Stakeholders in the Swiss health system M. Fiordelli 3
Life course epidemiology and public health – early life A. Camerini 3
Public health emergencies and preparedness R. Amati 3
Advanced research methods in public health M. Fiordelli / R. Amati 6
Public law I. Espa 3
Health economics evaluation G. Masiero 3
Electives*    
Total 2° Semester   27

3° Semester (Autumn)

Course Lecturer ECTS
Life course epidemiology and public health - late life M. Fiordelli                3
Dissemination of public health research findings R. Amati 3
Health informatics - 3
Data analysis P. Schulz 3
Health campaign development and evaluation A. Camerini 3
Databases F. Crestani 6
Digital health interventions M. Fiordelli 3
Electives*    
Total 3° Semester   24

4° Semester (Spring)

Course Lecturer ECTS
Thesis - 18
Field project - 9
Electives*    
Total 4° Semester   27
Total Master programme   120

* Electives
A total of 12 ECTS must be earn during the Master programme.

Semester Course Lecturer ECTS
Spring Data analytics F. Crestani 3
Spring Economics of well-being R. Odermatt 3
Spring Population aging and sustainability of the welfare state V. Galasso 3
Spring Philosophy and artificial intelligence J. Landgrebe / B. Smith 4
Autumn Management and innovation in public administration and the non-profit sector M. Barreca / M. Meneguzzo 6
Autumn Public administration challanges M. Barreca / Frey / M. Meneguzzo 3
Autumn Machine learning M. Wand 6