Associations between mobility patterns and COVID-19 deaths during the pandemic: A network structure and rank propagation modelling approach.
Array (N Y)
; 11: 100075, 2021 Sep.
Article
in English
| MEDLINE | ID: covidwho-1300624
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Array (N Y)
Year:
2021
Document Type:
Article
Affiliation country:
J.array.2021.100075
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