Abstract
Motivated by the ongoing 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. The relational event data modelling framework makes use of network statistics capturing the structure of movement events from and to several country’s municipalities. The fully probabilistic Bayesian approach allows to quantify the uncertainty associated to the relational tendencies explaining where and when movement events are established and where they are directed. The observed patient movements’ patterns at an early stage of the pandemic can provide interesting insights about the spread of the disease in the Asian country.
Highlights
The first case of COVID-19 infection in South Korea was reported on January 20, 2020 and was followed by a steady increase in the number of cases over the following month which made South Korea one of the first hardest-hit countries by the virus
We focus on the implementation of Bayesian dynamic network actor model (DyNAM) for the analysis of South Korea COVID-19 patient movement relational event data
In this paper we proposed a Bayesian estimation approach for dynamic network actor models in order to analyse the relational dynamics of the COVID-19 patient movements in South Korea at the beginning of 2020
Summary
The first case of COVID-19 infection in South Korea was reported on January 20, 2020 and was followed by a steady increase in the number of cases over the following month which made South Korea one of the first hardest-hit countries by the virus. We focus on the implementation of Bayesian DyNAMs for the analysis of South Korea COVID-19 patient movement relational event data. It has not been explored yet, a Bayesian treatment of this family of models is appropriate as it allows to quantify the uncertainty of the parameters associated to the network effects by building a fully probabilistic inferential framework. 6, we demonstrate the Monte Carlo inferential procedure by analysing the well-known Social Evolution dataset This example is meant to detail the use of prior specification in a context where previous information about the main network effects characterising the event dynamics is available from previous literature.
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