Corso di laurea magistrale Interateneo in Fisica dei sistemi complessi

Epidemiologia digitale

Digital Epidemiology

Anno accademico 2019/2020

Sommario insegnamento

• Obiettivi formativi
• Risultati dell'apprendimento attesi
• Modalità di insegnamento
• Modalità di verifica dell'apprendimento
• Programma
• Testi consigliati e bibliografia
• Strumenti didattici

Obiettivi formativi

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.gov/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. Moreover, the students will be guided on a few case studies with a hands-on approach.

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, Twitter etc.
• be able to design models for inferring and predicting epidemiological indicators from proxy data.

Modalità di insegnamento

Structure of the lectures: 3 hours, two times a week (⅓ of each section will be hands on).

(part of the course will be held in english)

Modalità di verifica dell'apprendimento

Exam: project + oral

Programma

Available Data: social media, sociopatterns, airtravel network, influenzanet (aggregated), official surveillance, census data, EHR (https://mimic.physionet.org/), prescription data (from UK)

Introduction to epidemiology and bio statistics (6h)

- history and evolution of epidemiology

- theoretical epidemiology, applied epidemiology, digital epidemiology

- core epidemiologic functions: public health surveillance, field investigation, analytic studies, evaluation and linkage, policy development

- descriptive epidemiology vs analytic epidemiology

- observational studies: cohort studies, cross-sectional studies, case-control studies

- disease occurrence and progression, natural history of a disease

- epidemic diseases, stages of an epidemic outbreak

- interventions

Network modeling (epidemic spreading, contact patterns, behavioral models, data-driven models) (21h)

- basic concepts in network science and social network analysis

- degree distributions, measures of centrality, clusters and communities

- features of real social networks and contact networks

- compartmental models

- epidemic processes on networks, epidemic threshold, reproductive number

- network heterogeneity & epidemic thresholds

- SIR / SIS / SEIR epidemic models

- metapopulation models & spatial models

- homophily, social selection, social influence, latent homophily

- computational social science & epidemiology

- sociopatterns and contact patterns

- realistic epidemiological models: GLEAM

- human contact networks from smartphone data, CDR data, wearable devices

- misinformation, opinion dynamics, echo chambers, vaccine adherence

Guest lecture on spatial viz: Rossano.

Digital Surveillance: Communicable, non-communicable diseases (18h)

- patterns for digital disease detection: proxy data, ground truth data sources, machine learning models

- the case of Google Flu Trends

- influenza prevalence from search engine queries (autoregressive model)

- influenza prevalence from Wikipedia page view data (generalized linear model)

- influenza prevalence from symptom mentions in Twitter (SVR model)

- vaccination coverage from sentiment analysis & geocoding of Twitter messages, opinions and polarization in relation to social network structure

- outbreak investigation systems: Healthmap, Promed, etc.

- the architecture of Healthmap

- syndromic surveillance using Web-based cohorts, early warning sentinels

- influenza prevalence from Web-based self-reported syndromic data

- pharmacovigilance: using search engine queries to discover adverse drug interactions

- validation

- obesity surveillance using location-based social network data

- mental health tracking, depression & suicide, eating disorders

- social media for tracking misinformation, opinion dynamics, echo chambers, vaccine adherence

- state of the art of sensors and wearables

- the future of clinical and physiological sensing

Privacy, security and ethics (2h)

- data collection, storage, sharing regulations

- genetic information

- social norms

- consumer applications, ongoing litigations

Seminar on Digital Medicine (1h)

https://www.ncbi.nlm.nih.gov/pubmed/?term=293

(more to come)

Orario lezioni

• Materiale didattico
• Bacheca appelli
• Vai a Moodle
• Visita i forum