Learning outcomes

This course aims to bring together different skills related to the mathematical modeling of infectious diseases, and in particular of covid-19.

 

Goals

The objective for the student would be to acquire a certain autonomy regarding the design and construction of a mathematical model on infectious diseases, using machine learning techniques applied to real data.
 

Content

  • General introduction to epidemiology (basis of epidemiology, characteristics of epidemiological data, biases)
  • Simple compartmental mathematical models (mass action principle, SIR, SIRS, SEIR models, non-constant population)
  • Complex compartmental mathematical models (age-structured models, social contact data, next generation matrix, susceptibility and infectiosity, variants of concern, vaccination campaigns)
  • Parameter estimation techniques (blackbox, likelihood, heuristic methods, sampling methods, MCMC, difficulties of calibration)
  • Scenarios and interpretations (projections and scenarios, model limitations, difficulties in interpreting and communicating results)
 

Teaching methods

Magistral lectures (4H to 6h/week) during the first weeks. Then, the student will have to implement their own model in a free language, under the supervision of an assistant.
 

Assessment method

The exam will consist of a presentation of the work carried out regarding the implementation of the model and the results obtained.
 
Details concerning the evaluation method are specified on the French language version of the descriptive sheet.
 

Sources, references and any support material

Slides on webcampus.
 
Handbook of Infectious Disease Data Analysis, Leonhard Held, Niel Hens, Philip O'Neill, Jacco Wallinga, Chapman & Hall/CRC handbooks of modern statistical methods, CRC Press, 2019
 

Language of instruction

French