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

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posted on 2021-09-06, 14:39 authored by Irini Furxhi, Arash Negahdari Kia, Darren ShannonDarren Shannon, Tim Jannusch, Finbarr Murphy, Barry Sheehan
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 propaga tion 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 as sociation 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.

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Publication

Array;11, 100075

Publisher

Elsevier

Note

peer-reviewed

Language

English

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