First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
LIFE DATA EPIDEMIOLOGY
SCP8082719, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2019/20
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination LIFE DATA EPIDEMIOLOGY
Website of the academic structure http://physicsofdata.scienze.unipd.it/2019/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge LEONARDO BADIA ING-INF/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP8084321 LIFE DATA EPIDEMIOLOGY LEONARDO BADIA IN2371
INP8084321 LIFE DATA EPIDEMIOLOGY LEONARDO BADIA IN2371
INP8084321 LIFE DATA EPIDEMIOLOGY LEONARDO BADIA IN2371
INP8084321 LIFE DATA EPIDEMIOLOGY LEONARDO BADIA IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SECS-S/01 Statistics 6.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2018 course timetable

Syllabus
Prerequisites: The course requires some previous knowledge on:
- Probability theory.
- Differential equations.
Target skills and knowledge: The course aims at acquiring know-how about the main mathematical models for epidemiology and available data in public health scenario. The description of existing models will not be limited to this field of application but also web and social media will also be explored as possible scenarios of viral trends.
Students will acquire analytical and mathematical characterization skills, as well as the ability to read data from various epidemiology contexts.
As a result, the students will become competent in terms of abstracting and applying the models presented during the lectures to many scenarios, through both quantitative assessments and practical group-based projects.
Examination methods: The exam will consist of two parts.
1) an individual written exam with exercises on mathematical evaluations and practical applications of concepts explained during the course
2) a (group) project developed throughout the course and discussed after the written exam

The two parts of the exam can be sustained separately, although it is advised that the students perform them together (typically, exam sessions will have both parts in the order above)
Assessment criteria: The written exam assigns up to 24 points and must be sufficient in order to evaluate the project. The project assigns up to 10 points.

The written exam is judged upon its relevance, correctness, numerical precision, and level of detail of the answers given.
The project is evaluated based on adherence of the development to the required task, originality and correctness of the development, clarity of exposition, and the overall quality of the presentation.
Course unit contents: Epidemics: motivation and applications (both to life sciences and ICT)
Epidemics through compartmental models
Solutions of epidemic models through differential equations
Demography and equilibria
Extended models and complex contagions
Time-variable trends and temporal networks
Network epidemics
Metapopulation for spatial diffusion
Data-driven models and integration in computational epidemology
Epidemiology data: surveillance, problems, and biases
Statistical and mechanical methods
Maximum likelihood fit
Public health scenarios: analysis and forecasts
Planned learning activities and teaching methods: The course will be offered through frontal lectures with the aid of electronic material. Some lectures could be held in a computer laboratory as guided experiences replied step-by-step according to the lecturers' instructions.
All the material will be supplied in electronic format to allow the students to review it with their individual study.
Additional notes about suggested reading: All the material will be provided through the course webpage (moodle): notes, additional material, experimental data, exercises.
Some textbooks will be also available for references (for selected chapters).
Textbooks (and optional supplementary readings)
  • Keeling, Matt J.; Rohani, Pejman, Modeling infectious diseases in humans and animalsMatt J. Keeling and Pejman Rohani. Princeton: N.J., Oxford, Princeton University Press, 2008. Cerca nel catalogo
  • Kiss, István Z., Miller, Joel, Simon, Péter L., Mathematics of Epidemics on Networks. --: Springer, 2017. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)
  • Matlab

Sustainable Development Goals (SDGs)
Good Health and Well-Being Quality Education