Journal Home > Volume 5 , Issue 4
Big Data Mining and Analytics 2022, 5(4): 318-338 https://doi.org/10.26599/BDMA.2022.9020010
Open Access | Issue | Published: 18 July 2022

Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

Show Author's Information Tianlong Zheng,  Chunli Zhang,  Yueting Shi,  Debao Chen,  Sheng Liu( )
School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China
School of Economics and Management, Tiangong University, Tianjin 300000, China
Keywords:
COVID-19, data analysis, extreme random forest regression, spectral clustering, HDI
Cite this article:
Zheng T, Zhang C, Shi Y, et al. Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis. Big Data Mining and Analytics, 2022, 5(4): 318-338. https://doi.org/10.26599/BDMA.2022.9020010
Download citation
EndNote(RIS)
BibTeX

674

Views

83

Downloads

Citations

3

Crossref

4

WoS

4

Scopus

0

CSCD

Abstract Full text About this article

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.


menu
Abstract
Full text
Outline
About this article

Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

Show Author's information Tianlong Zheng, Chunli Zhang, Yueting Shi, Debao Chen, Sheng Liu( )
School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China
School of Economics and Management, Tiangong University, Tianjin 300000, China

Abstract

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.

Keywords: COVID-19, data analysis, extreme random forest regression, spectral clustering, HDI

References(54)

[1]
C. R. Wells, P. Sah, S. M. Moghadas, A. Pandey, A. Shoukat, Y. Wang, Z. Wang, L. A. Meyers, B. H. Singer, and A. P. Galvani, Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak, Proc. Natl. Acad. Sci. USA, vol. 117, no. 13, pp. 7504–7509, 2020.
DOI Google Scholar
[2]
D. S. Candido, I. M. Claro, J. G. De Jesus, W. M. Souza, F. R. R. Moreira, S. Dellicour, T. A. Mellan, L. Du Plessis, R. H. M. Pereira, F. C. S. Sales, et al., Evolution and epidemic spread of SARS-CoV-2 in Brazil, Science, vol. 369, no. 6508, pp. 1255–1260, 2020.
DOI Google Scholar
[3]
G. E. Patterson, K. M. McIntyre, H. E. Clough, and J. Rushton, Societal impacts of pandemics: Comparing COVID-19 with history to focus our response, Front. Public Health, vol. 9, p. 630449, 2021.
DOI Google Scholar
[4]
G. G. Trindade, S. M. C. Caxito, A. R. E. O. Xavier, M. A. S. Xavier, and F. BrandÃo, COVID-19: Therapeutic approaches description and discussion, An. Acad. Bras. Ciênc., vol. 92, no. 2, p. e20200466, 2020.
DOI Google Scholar
[5]
S. Flaxman, S. Mishra, A. Gandy, H. J. T. Unwin, T. A. Mellan, H. Coupland, C. Whittaker, H. Zhu, T. Berah, J. W. Eaton, et al., Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, vol. 584, no. 7820, pp. 257–261, 2020.
Google Scholar
[6]
N. Askitas, K. Tatsiramos, and B. Verheyden, Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study, Sci. Rep., vol. 11, no. 1, p. 1972, 2021.
DOI Google Scholar
[7]
R. A. Roman, Application of spectral clustering for the detection of high priority areas of attention for COVID-19 in Mexico, in Proc. 4th EAI Int. Conf. Computer Science and Health Engineering in Health Services, Cham, Switzerland, 2020, pp. 130–142.
DOI Google Scholar
[8]
Z. Malki, E. S. Atlam, A. Ewis, G. Dagnew, A. R. Alzighaibi, G. ELmarhomy, M. A. Elhosseini, A. E. Hassanien, and I. Gad, ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound, Neural Comput. Appl., vol. 33, no. 7, pp. 2929–2948, 2021.
DOI Google Scholar
[9]
H. Fransiska, Clustering provinces in Indonesia based on daily Covid-19 cases, in Proc. Int. Conf. Mathematics, Statistics and Data Science, Bogor, Indonesia, 2020, pp. 1–9.
DOI Google Scholar
[10]
M. Coccia, Preparedness of countries to face COVID-19 pandemic crisis: Strategic positioning and factors supporting effective strategies of prevention of pandemic threats, Environ. Res., vol. 203, p. 111678, 2022.
DOI Google Scholar
[11]
M. F. Bashir, B. J. Ma, , B. Komal, M. A. Bashir, T. H. Farooq, N. Iqbal, and M. Bashir, Correlation between environmental pollution indicators and COVID-19 pandemic: A brief study in Californian context, Environ. Res., vol. 187, p. 109652, 2020.
DOI Google Scholar
[12]
S. E. Haque and M. Rahman, Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh, Environ. Sci. Policy, vol. 114, pp. 253–255, 2020.
DOI Google Scholar
[13]
N. Islam, Q. Bukhari, Y. Jameel, S. Shabnam, A. M. Erzurumluoglu, M. A. Siddique, J. M. Massaro, and R. B. D’Agostino Sr., COVID-19 and climatic factors: A global analysis, Environ. Res., vol. 193, p. 110355, 2021.
DOI Google Scholar
[14]
N. James and M. Menzies, Cluster-based dual evolution for multivariate time series: Analyzing COVID-19, Chaos, vol. 30, no. 6, p. 061108, 2020.
DOI Google Scholar
[15]
M. R. Mahmoudi, D. Baleanu, Z. Mansor, B. A. Tuan, and K. H. Pho, Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries, Chaos Solitons Fractals, vol. 140, p. 110230, 2020.
DOI Google Scholar
[16]
N. Kadi and M. Khelfaoui, Population density, a factor in the spread of COVID-19 in Algeria: Statistic study, Bull. Natl. Res. Cent., vol. 44, no. 1, p. 138, 2020.
DOI Google Scholar
[17]
K. S. Bhangu, J. K. Sandhu, and L. Sapra, Time series analysis of COVID-19 cases, World J. Eng., vol. 19, no. 1, pp. 40–48, 2022.
DOI Google Scholar
[18]
S. Asfahan, A. Shahul, G. Chawla, N. Dutt, R. Niwas, and N. Gupta, Early trends of socio-economic and health indicators influencing case fatality rate of COVID-19 pandemic, Monaldi Arch. Chest. Dis., vol. 90, no. 3, p. 1388, 2020.
DOI Google Scholar
[19]
M. Gilbert, G. Pullano, F. Pinotti, E. Valdano, C. Poletto, P. Y. Boëlle, E. D’Ortenzio, Y. Yazdanpanah, S. P. Eholie, M. Altmann, et al., Preparedness and vulnerability of African countries against importations of COVID-19: A modelling study, Lancet, vol. 395, no. 10227, pp. 871–877, 2020.
DOI Google Scholar
[20]
A. Rahman, N. Zaman, A. T. Asyhari, F. Al-Turjman, Z. Alam Bhuiyan, and M. F. Zolkipli, Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices, Sustain Cities Soc., vol. 62, p. 102372, 2020.
DOI Google Scholar
[21]
A. J. Jawad, Effectiveness of population density as natural social distancing in COVID-19 spreading, Ethics Med. Public Health, vol. 15, p. 100556, 2020.
DOI Google Scholar
[22]
R. Azizah, S. Martini, L. Sulistyorini, , A. S. Pawitra, D. Budijanto, S. S. Nagari, C. Fitrahanjani, F. H. Ramadhani, and M. T. Latif, Association between climatic conditions, population density and COVID-19 in Indonesia, Sains Malays., vol. 50, no. 3, pp. 879–887, 2021.
DOI Google Scholar
[23]
J. G. Xie and Y. J. Zhu, Association between ambient temperature and COVID-19 infection in 122 cities from China, Sci. Total Environ., vol. 724, p. 138201, 2020.
DOI Google Scholar
[24]
M. Xu, C. Cao, X. Zhang, H. Lin, Z. Yao, S. Zhong, Z. Huang, and R. Shea Duerler, Fine-scale space-time cluster detection of COVID-19 in Mainland China using retrospective analysis, Int.J. Environ. Res. Public Health, vol. 18, no. 7, p. 3583, 2021.
DOI Google Scholar
[25]
D. Pan, S. Sze, J. S. Minhas, M. N. Bangash, N. Pareek, P. Divall, C. M. L. Williams, M. R. Oggioni, I. B. Squire, L. B. Nellums, et al., The impact of ethnicity on clinical outcomes in COVID-19: A systematic review, eClinicalMedicine, vol. 23, p. 100404, 2020.
DOI Google Scholar
[26]
G. Saini, M. H. Swahn, and R. Aneja, Disentangling the coronavirus disease 2019 health disparities in African Americans: Biological, environmental, and social factors, Open Forum Infect. Dis., vol. 8, no. 3, p. ofab064, 2021.
DOI Google Scholar
[27]
HDR Technical Note, https://www.hdr.undp.org/sites/default/files/hdr2020_technical_notes.pdf, 2020.
[28]
State of Global Air /2020, https://www.stateofglobalair.org/sites/default/files/documents/2020-10/soga-2020-report.pdf, 2020.
[29]
Global Health Security Index, https://www.ghsindex.org/wp-content/uploads/2019/10/2019-Global-Health-Security-Index.pdf, 2019.
[30]
GBD 2016 Healthcare Access and Quality Collaborators, Measuring performance on the Healthcare Access and Quality Index for 195 countries and territories and selected subnational locations: A systematic analysis from the Global Burden of Disease Study 2016, Lancet, vol. 391, no. 10136, pp. 2236–2271, 2018.
Google Scholar
[31]
B. T. Polyak and A. B. Juditsky, Acceleration of stochastic approximation by averaging, SIAM J. Control Optim., vol. 30, no. 4, pp. 838–855, 1992.
DOI Google Scholar
[32]
C. Cortes and V. Vapnik, Support-vector networks, Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
DOI Google Scholar
[33]
T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, 1967.
DOI Google Scholar
[34]
H. Li, Statistical Learning Methods, (in Chinese), Beijing, China: Tsinghua University Press, 2012, pp. 67–73.
[35]
E. Scornet, Tuning parameters in random forests, ESAIM: Proc. Surv., vol. 60, pp. 144–162, 2017.
DOI Google Scholar
[36]
P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Mach. Learn., vol. 63, no. 1, pp. 3–42, 2006.
DOI Google Scholar
[37]
J. H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Statist., vol. 29, no. 5, pp. 1189–1232, 2001.
DOI Google Scholar
[38]
L. van der Maaten and G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res., vol. 9, pp. 2579–2605, 2008.
Google Scholar
[39]
G. Hinton and S. Roweis, Stochastic neighbor embedding, Adv. Neural Inf. Process. Syst., vol. 41, no. 4, pp. 833–840, 2002.
Google Scholar
[40]
C. Xie, S. Wu, C. Zhang, W. Sun, H. He, T. Pei, and G. Luo, Analysis of time series features of COVID-19 in various countries based on pedigree clustering, (in Chinese), J. Geo-Inf. Sci., vol. 23, no. 2, pp. 236–245, 2021.
Google Scholar
[41]
U. von Luxburg, A tutorial on spectral clustering, Statist. Comput., vol. 17, no. 4, pp. 395–416, 2007.
DOI Google Scholar
[42]
H. Yin, T. Sun, L. Yao, Y. Jiao, L. Ma, L. Lin, J. C. Graff, L. Aleya, A. Postlethwaite, W. Gu, and H. Chen, Association between population density and infection rate suggests the importance of social distancing and travel restriction in reducing the COVID-19 pandemic, Environ. Sci. Pollut. Res., vol. 28, no. 30, pp. 40424–40430, 2021.
DOI Google Scholar
[43]
M. Coccia, The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19, Int.J. Environ. Stud., vol. 78, no. 1, pp. 1–27, 2021.
DOI Google Scholar
[44]
M. S. Shiels, J. S. Almeida, M. García-Closas, P. S. Albert, N. D. Freedman, and A. Berrington de Gonzalez, Impact of population growth and aging on estimates of excess U.S. deaths during the COVID-19 Pandemic, March to August 2020, Ann. Intern. Med., vol. 174, no. 4, pp. 437–443, 2021.
DOI Google Scholar
[45]
T. Caliski and J. Harabasz, A dendrite method for cluster analysis, Commun. Statist., vol. 3, no. 1, pp. 1–27, 1974.
DOI Google Scholar
[46]
M. Coccia, How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atmos. Pollut. Res., vol. 12, no. 1, pp. 437–445, 2021.
DOI Google Scholar
[47]
J. N. Nkengasong and W. Mankoula, Looming threat of COVID-19 infection in Africa: Act collectively, and fast, Lancet, vol. 395, no. 10227, pp. 841–842, 2020.
DOI Google Scholar
[48]
M. M. Kavanagh, N. A. Erondu, O. Tomori, V. J. Dzau, E. A. Okiro, A. Maleche, I. C. Aniebo, U. Rugege, C. B. Holmes, and L. O. Gostin, Access to lifesaving medical resources for African countries: COVID-19 testing and response, ethics, and politics, Lancet, vol. 395, no. 10238, pp. 1735–1738, 2020.
DOI Google Scholar
[49]
Y. Diao, S. Kodera, D. Anzai, J. Gomez-Tames, E. A. Rashed, and A. Hirata, Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan, One Health, vol. 12, p. 100203, 2021.
DOI Google Scholar
[50]
M. Coccia, The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system, Sci. Total Environ., vol. 775, p. 145801, 2021.
DOI Google Scholar
[51]
D. K. A. Rosario, Y. S. Mutz, P. C. Bernardes, and C. A. Conte-Junior, Relationship between COVID-19 and weather: Case study in a tropical country, Int.J. Hyg. Environ. Health, vol. 229, p. 113587, 2020.
DOI Google Scholar
[52]
M. Coccia, Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID, Sci. Total Environ., vol. 729, p. 138474, 2020.
DOI Google Scholar
[53]
M. Coccia, The impact of first and second wave of the COVID-19 pandemic in society: Comparative analysis to support control measures to cope with negative effects of future infectious diseases, Environ. Res., vol. 197, p. 111099, 2021.
DOI Google Scholar
[54]
E. A. Harrison and J. W. Wu, Vaccine confidence in the time of COVID-19, Eur.J. Epidemiol., vol. 35, no. 4, pp. 325–330, 2020.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 08 September 2021
Revised: 20 February 2022
Accepted: 18 April 2022
Published: 18 July 2022
Issue date: December 2022

Copyright

© The author(s) 2022.

Acknowledgements

This work was supported in part by the National University Student Innovation and Entrepreneurship in Training Program of China (No. 202110373044), the National Natural Science Foundation of China (No. 71801108), the Laboratory Opening Project Fund of Huaibei Normal University (No. 2021sykf041), and the Special Needs Project of Huaibei Normal University (No. 2021zlgc147).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return