Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach

Furxhi Irini; Arash Negahdari Kia; Darren Shannon; Tim Jannusch; Finbarr Murphy; Barry Sheehan

doi:10.1016/j.array.2021.100075

Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach

Array (N Y). 2021 Sep:11:100075. doi: 10.1016/j.array.2021.100075. Epub 2021 Jul 7.

Authors

Furxhi Irini^{1

2}, Arash Negahdari Kia², Darren Shannon², Tim Jannusch^{2

3}, Finbarr Murphy^{2

1}, Barry Sheehan²

Affiliations

¹ Transgero Limited, Newcastle West, Limerick, Ireland.
² Kemmy Business School, University of Limerick, Ireland.
³ Institut for Insurance Studies, TH, Köln, Germany.

Abstract

Background: From February 2020, both urban and rural Ireland witnessed the rapid proliferation of the COVID-19 disease throughout its counties. During this period, the national COVID-19 responses included stay-at-home directives issued by the state, subject to varying levels of enforcement.

Methods: In this paper, we present a new method to assess and rank the causes of Ireland COVID-19 deaths as it relates to mobility activities within each county provided by Google while taking into consideration the epidemiological confirmed positive cases reported per county. We used a network structure and rank propagation modelling approach using Personalised PageRank to reveal the importance of each mobility category linked to cases and deaths. Then a novel feature-selection method using relative prominent factors finds important features related to each county's death. Finally, we clustered the counties based on features selected with the network results using a customised network clustering algorithm for the research problem.

Findings: Our analysis reveals that the most important mobility trend categories that exhibit the strongest association to COVID-19 cases and deaths include retail and recreation and workplaces. This is the first time a network structure and rank propagation modelling approach has been used to link COVID-19 data to mobility patterns. The infection determinants landscape illustrated by the network results aligns soundly with county socio-economic and demographic features. The novel feature selection and clustering method presented clusters useful to policymakers, managers of the health sector, politicians and even sociologists. Finally, each county has a different impact on the national total.

Keywords: COVID-19; Feature selection; Hierarchical clustering; Mobility reports; Network modelling; Personalised PageRank.