Corso di laurea magistrale Interateneo in Fisica dei sistemi complessi

Epidemiologia digitale

Digital Epidemiology

Anno accademico 2024/2025

Sommario insegnamento

• Obiettivi formativi
• Risultati dell'apprendimento attesi
• Programma
• Modalità di insegnamento
• Modalità di verifica dell'apprendimento
• Testi consigliati e bibliografia
• Orario lezioni
• Strumenti didattici
• Scheda del corso

Obiettivi formativi

This course provides an overview of digital data and computational methods that can be leveraged to study population health at scale. 

Risultati dell'apprendimento attesi

By the end of the course the students will have the following:

• have a knowledge of the state-of-the-art research in the young and growing field of Digital Epidemiology.
• be able to collect data from digital proxy sources (such as Google Trends, Wikipedia, news articles, Twitter)
• be able to design and implement computational models for representing and predicting the spread of infectious disease
• be able to integrate data and advanced statistical techniques into epidemic models

Programma

This course provides an overview of digital data and computational methods that can be leveraged to study population health at scale. 

The first segment will focus on digital epidemiology, offering an introductory exploration to the discipline, tracing its recent evolution, and presenting a variety of use cases. In particular, this module will focus both on passively collected digital data (i.e., social media data, internet searches) and actively collected data via participatory surveillance platforms for the study of infectious diseases.

The second part of the course will focus on computational epidemiology, offering a comprehensive exploration of various mathematical and computational techniques employed to model and predict the transmission of infectious diseases. Specifically, we will delve into simple single population compartmental models, network models, and metapopulation models. More advanced modeling concepts, such as contact matrices and behavioural changes, will also be discussed. The course will emphasise the practical implementation of these models, incorporating Bayesian model calibration methods, principles of epidemic forecasting, data integration to enhance model accuracy, and the analysis of real-world epidemic scenarios, including the COVID-19 pandemic.

About one-third of the course lectures will be dedicated to practical, hands-on sessions utilising the Python programming language. While the primary focus of the course centres on the dynamics of infectious disease transmission within human populations, the methodologies introduced hold versatile applicability, readily extending to a wide array of subjects. These include non-human populations and various spreading processes such as the dissemination of information, propagation of ideas, adoption of norms, and the circulation of content on the Internet.

Modalità di insegnamento

The course will be in person, with about one-third of classes dedicated to hands-on Python lab sessions

Modalità di verifica dell'apprendimento

The examination will consist in a practical project + oral examination on materials presented in class

Modeling Infectious Diseases in Humans and Animals
Matt J. Keeling and Pejman Rohani (https://www.jstor.org/stable/j.ctvcm4gk0)

Orario lezioni

• Materiale didattico
• Bacheca appelli
• Orario lezioni
• Vai a Moodle
• Registrati a questo insegnamento
• Studenti registrati
• Invia email agli studenti registrati
• Esporta tabella