Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis
The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and contr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2022-12-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020010 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832573624367710208 |
|---|---|
| author | Tianlong Zheng Chunli Zhang Yueting Shi Debao Chen Sheng Liu |
| author_facet | Tianlong Zheng Chunli Zhang Yueting Shi Debao Chen Sheng Liu |
| author_sort | Tianlong Zheng |
| collection | DOAJ |
| description | The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic. Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models. Results showed that the extreme random forest regression (ERFR) model had the best performance, and factors such as population density, ozone, median age, life expectancy, and Human Development Index (HDI) were relatively influential on the spread and diffusion of COVID-19 in the ERFR model. In addition, the epidemic clustering characteristics were analyzed through the spectral clustering algorithm. The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered, and its clustering characteristics and influencing factors also exhibited some consistency in distribution. This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies. |
| format | Article |
| id | doaj-art-7265e510f3e24e378762b30d268c94d5 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7265e510f3e24e378762b30d268c94d52025-02-02T03:45:08ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-12-015431833810.26599/BDMA.2022.9020010Influencing Factors and Clustering Characteristics of COVID-19: A Global AnalysisTianlong Zheng0Chunli Zhang1Yueting Shi2Debao Chen3Sheng Liu4School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, ChinaSchool of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, ChinaSchool of Economics and Management, Tiangong University, Tianjin 300000, ChinaSchool of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, ChinaSchool of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, ChinaThe unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic. Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models. Results showed that the extreme random forest regression (ERFR) model had the best performance, and factors such as population density, ozone, median age, life expectancy, and Human Development Index (HDI) were relatively influential on the spread and diffusion of COVID-19 in the ERFR model. In addition, the epidemic clustering characteristics were analyzed through the spectral clustering algorithm. The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered, and its clustering characteristics and influencing factors also exhibited some consistency in distribution. This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies.https://www.sciopen.com/article/10.26599/BDMA.2022.9020010data analysisextreme random forest regressionspectral clusteringhdicovid-19 |
| spellingShingle | Tianlong Zheng Chunli Zhang Yueting Shi Debao Chen Sheng Liu Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis Big Data Mining and Analytics data analysis extreme random forest regression spectral clustering hdi covid-19 |
| title | Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis |
| title_full | Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis |
| title_fullStr | Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis |
| title_full_unstemmed | Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis |
| title_short | Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis |
| title_sort | influencing factors and clustering characteristics of covid 19 a global analysis |
| topic | data analysis extreme random forest regression spectral clustering hdi covid-19 |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020010 |
| work_keys_str_mv | AT tianlongzheng influencingfactorsandclusteringcharacteristicsofcovid19aglobalanalysis AT chunlizhang influencingfactorsandclusteringcharacteristicsofcovid19aglobalanalysis AT yuetingshi influencingfactorsandclusteringcharacteristicsofcovid19aglobalanalysis AT debaochen influencingfactorsandclusteringcharacteristicsofcovid19aglobalanalysis AT shengliu influencingfactorsandclusteringcharacteristicsofcovid19aglobalanalysis |