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Measuring of the COVID-19 Based on Time-Geography
At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are r...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507668/ https://www.ncbi.nlm.nih.gov/pubmed/34639612 http://dx.doi.org/10.3390/ijerph181910313 |
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author | Yin, Zhangcai Huang, Wei Ying, Shen Tang, Panli Kang, Ziqiang Huang, Kuan |
author_facet | Yin, Zhangcai Huang, Wei Ying, Shen Tang, Panli Kang, Ziqiang Huang, Kuan |
author_sort | Yin, Zhangcai |
collection | PubMed |
description | At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy. |
format | Online Article Text |
id | pubmed-8507668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85076682021-10-13 Measuring of the COVID-19 Based on Time-Geography Yin, Zhangcai Huang, Wei Ying, Shen Tang, Panli Kang, Ziqiang Huang, Kuan Int J Environ Res Public Health Article At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy. MDPI 2021-09-30 /pmc/articles/PMC8507668/ /pubmed/34639612 http://dx.doi.org/10.3390/ijerph181910313 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yin, Zhangcai Huang, Wei Ying, Shen Tang, Panli Kang, Ziqiang Huang, Kuan Measuring of the COVID-19 Based on Time-Geography |
title | Measuring of the COVID-19 Based on Time-Geography |
title_full | Measuring of the COVID-19 Based on Time-Geography |
title_fullStr | Measuring of the COVID-19 Based on Time-Geography |
title_full_unstemmed | Measuring of the COVID-19 Based on Time-Geography |
title_short | Measuring of the COVID-19 Based on Time-Geography |
title_sort | measuring of the covid-19 based on time-geography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507668/ https://www.ncbi.nlm.nih.gov/pubmed/34639612 http://dx.doi.org/10.3390/ijerph181910313 |
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