Antonio Mario Arrizza (University of Bologna)

will speak on

Bayesian dynamic network actor models with application to South Korean COVID-19 patient movement data

Time: 12:00PM
Date: Mon 30th November 2020
Location: Online [map]

Abstract: The relational event data modelling framework is an increasingly popular approach to the analysis of relational dynamics and has been adopted by network scientists in a wide range of applications. Motivated by the recent COVID-19 pandemic, this article introduces Bayesian dynamic network actor models for the analysis of infected individuals movements in South Korea during the first three months of 2020. This fully probabilistic modelling approach allows to identify the important relational features explaining where and when new movement ties are established and where these ties are directed.
The dataset displays patient movements at an early stage of the pandemic, thus providing interesting insights about the spread of the disease in the Asian country.

Join Zoom Meeting: https://ucd-ie.zoom.us/j/63946808644

Meeting ID: 639 4680 8644
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(This talk is part of the Working Group on Statistical Learning series.)

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