Digital epidemiology: modelling and predicting epidemics in the age of big-data

Friday, 12 January 2018 (10:00-18:00)

Instructors: Dr Nicola Perra (University of Greenwich)

Nowadays more than 3 billion of people had access to the Internet. About 7 billion phone subscriptions have been activated, and more than 20% of them are associated to smartphones. As result a large fraction of our activities is digital, often online. Think about the way we communicate using social networks, emails, blog posts etc. Or about the way we access information via countless online resources that we can efficiently mine using search engines. Furthermore, the miniaturisation of devices created a wide range of wearables able to measure our interactions, movements, as well as vital signs.
 
As result, the large amount of data we generate, or that we can collect, contains crucial epidemiological indicators. Just to mention few examples think about people speaking about their health on social networks, or searching for diseases' symptoms on search engines. Think about the uses of GPS data to characterise human mobility at different geographical scales, of RFID tags to identify the features of human face-to-face contacts, or of mobile apps to gather health information from users that sign-up. 
 
All together these new technologies and data provide unprecedented opportunities to model and predict epidemics. In this context, the course will provide an introduction to different epidemiological models fuelled by the big-data revolution. 

Learning outcomes

The course will provide a basic introduction to Python that will be used throughout the module. The students will be guided through two "hands on" case studies. In the first, participants will learn how to use data mining and machine learning methods applied to posts on social media to predict Influenza Like Illnesses (ILIs). In the second students, will learn how to use network science to develop agent-based models and characterise the spreading of infectious disease in groups of individuals. Finally, participants will learn how to use GLEAM: the global epidemic and mobility model. This is a unique data-driven realistic epidemiological model able to predict the spreading of infectious diseases at the global scale.

Please register here and you will be contacted to make payment.

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