Cargando…

The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces

The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus’ rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing mov...

Descripción completa

Detalles Bibliográficos
Autores principales: Hass, Frederik Seeup, Jokar Arsanjani, Jamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998460/
https://www.ncbi.nlm.nih.gov/pubmed/33802001
http://dx.doi.org/10.3390/ijerph18062803
_version_ 1783670557340860416
author Hass, Frederik Seeup
Jokar Arsanjani, Jamal
author_facet Hass, Frederik Seeup
Jokar Arsanjani, Jamal
author_sort Hass, Frederik Seeup
collection PubMed
description The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus’ rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability events with massive impacts. Certainly, similar to many phenomena, the Covid-19 pandemic has shown us its own geography presenting its emergence and evolving patterns as well as taking advantage of our geographical settings for escalating its spread. Hence, this study aims at presenting a data-driven approach for exploring the spatio-temporal patterns of the pandemic over a regional scale, i.e., Europe and a country scale, i.e., Denmark, and also what geographical variables potentially contribute to expediting its spread. We used official regional infection rates, points of interest, temperature and air pollution data for monitoring the pandemic’s spread across Europe and also applied geospatial methods such as spatial autocorrelation and space-time autocorrelation to extract relevant indicators that could explain the dynamics of the pandemic. Furthermore, we applied statistical methods, e.g., ordinary least squares, geographically weighted regression, as well as machine learning methods, e.g., random forest for exploring the potential correlation between the chosen underlying factors and the pandemic spread. Our findings indicate that population density, amenities such as cafes and bars, and pollution levels are the most influential explanatory variables while pollution levels can be explicitly used to monitor lockdown measures and infection rates at country level. The choice of data and methods used in this study along with the achieved results and presented discussions can empower health authorities and decision makers with an interactive decision support tool, which can be useful for imposing geographically varying lockdowns and protectives measures using historical data.
format Online
Article
Text
id pubmed-7998460
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79984602021-03-28 The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces Hass, Frederik Seeup Jokar Arsanjani, Jamal Int J Environ Res Public Health Article The Covid-19 pandemic emerged and evolved so quickly that societies were not able to respond quickly enough, mainly due to the nature of the Covid-19 virus’ rate of spread and also the largely open societies that we live in. While we have been willingly moving towards open societies and reducing movement barriers, there is a need to be prepared for minimizing the openness of society on occasions such as large pandemics, which are low probability events with massive impacts. Certainly, similar to many phenomena, the Covid-19 pandemic has shown us its own geography presenting its emergence and evolving patterns as well as taking advantage of our geographical settings for escalating its spread. Hence, this study aims at presenting a data-driven approach for exploring the spatio-temporal patterns of the pandemic over a regional scale, i.e., Europe and a country scale, i.e., Denmark, and also what geographical variables potentially contribute to expediting its spread. We used official regional infection rates, points of interest, temperature and air pollution data for monitoring the pandemic’s spread across Europe and also applied geospatial methods such as spatial autocorrelation and space-time autocorrelation to extract relevant indicators that could explain the dynamics of the pandemic. Furthermore, we applied statistical methods, e.g., ordinary least squares, geographically weighted regression, as well as machine learning methods, e.g., random forest for exploring the potential correlation between the chosen underlying factors and the pandemic spread. Our findings indicate that population density, amenities such as cafes and bars, and pollution levels are the most influential explanatory variables while pollution levels can be explicitly used to monitor lockdown measures and infection rates at country level. The choice of data and methods used in this study along with the achieved results and presented discussions can empower health authorities and decision makers with an interactive decision support tool, which can be useful for imposing geographically varying lockdowns and protectives measures using historical data. MDPI 2021-03-10 /pmc/articles/PMC7998460/ /pubmed/33802001 http://dx.doi.org/10.3390/ijerph18062803 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hass, Frederik Seeup
Jokar Arsanjani, Jamal
The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title_full The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title_fullStr The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title_full_unstemmed The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title_short The Geography of the Covid-19 Pandemic: A Data-Driven Approach to Exploring Geographical Driving Forces
title_sort geography of the covid-19 pandemic: a data-driven approach to exploring geographical driving forces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998460/
https://www.ncbi.nlm.nih.gov/pubmed/33802001
http://dx.doi.org/10.3390/ijerph18062803
work_keys_str_mv AT hassfrederikseeup thegeographyofthecovid19pandemicadatadrivenapproachtoexploringgeographicaldrivingforces
AT jokararsanjanijamal thegeographyofthecovid19pandemicadatadrivenapproachtoexploringgeographicaldrivingforces
AT hassfrederikseeup geographyofthecovid19pandemicadatadrivenapproachtoexploringgeographicaldrivingforces
AT jokararsanjanijamal geographyofthecovid19pandemicadatadrivenapproachtoexploringgeographicaldrivingforces