Digital Epidemiology (4567)
  
Coordinating lecturer :Prof. Daniela PAOLOTTI 
  
Co-lecturer :Prof. dr. Niel HENS 
 dr. Pietro COLETTI 

Language of instruction : English
Credits: 3,0
  
Period: quarter 3 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   No sequentiality

Content

The pervasiveness of the Web and mobile technologies as well as the growing adoption of smart wearable sensors have significantly changed the landscape of epidemic intelligence data gathering with an unprecedented impact on global public health. The digital traces generated by a large number of individuals interacting with Web and mobile technologies as well as wearable sensors contain epidemiological indicators that are not easily accessed with traditional approaches. Collectively, these digital sources largely enhance the capabilities of epidemiology and global public health. Indeed, since more than a decade, the growing field of digital epidemiology (https://www.ncbi.nlm.nih.g ov/pubmed/?term=29302758) has been using digital data generated outside the public health system to carry out epidemiological studies on an unprecedented scale and with an unprecedented precision.
The goal of this course is to provide an overview of the main success stories collected during the first ten years of this young and exciting field. During the course, the students will be guided through popular methodologies and approaches in digital epidemiology with a focus on a few case studies with a hands-on approach.


Organisational and teaching methods
Organisational methods  
Collective feedback moment ✔  
Lecture ✔  
Project ✔  
Small group session ✔  


Evaluation

Quarter 3 (3,00sp)

Evaluation method
Oral exam100 %
Open questions✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Recommended course material
 

A list of recommended reading papers will be communicated on Blackboard. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

    • The student can critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  EC 

    • The student has the habit to assess data quality and integrity. 

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 
    		• The student is able to extract new knowledge and insights from datasets in the application domain.
     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.

  •  EC 

    • The student is able to efficiently acquire, store and process data.

     
  •  DC 
    		• ... selecting and using the best data management options
     
  •  DC 
    		• ...maintain provenance of data, analyses and results
  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective oral communicator in their own field.

     
  •  DC 

    • The student is an effective writer in their own field.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly.

     
  •  DC 

    • The student acts according to societal and ethical standards in general and particularly within the fields of statistics and data science.

     
  •  DC 

    • The student can explain ethical issues and dilemmas within the fields of statistics and data science.

     
  •  DC 

    • The student respects the privacy of data, people and organizations with whom he/she comes into direct or indirect contact.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

  •  EC 

    • The student knows the societal relevance of statistics and data science.

     
  •  DC 

    • The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization

     
  •  DC 

    • The student can reflect on societal tendencies, particularly within the programme specialization.

  •  EC 

    • The student routinely monitors his/her own learning process and adjusts and improves it accordingly.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Biostatistics J
2nd year Master Data Science J
2nd year Master Quantitative Epidemiology J
2nd year Master Quantitative Epidemiology - icp J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.