Similarities among countries during the COVID-19 pandemic

Abstract

On Jan 30, 2020, The World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) epidemic a Public Health Emergency of International Concern. The new type of coronavirus (2019-nCoV) is a new virus among viruses under the name. The novel coronavirus disease 2019 (COVID-19) pandemic has spread from China to 25 countries. This study aims to identify the countries that seem similar to each other by examining their situations during the COVID-19 process. For this purpose, cluster analysis was performed for 30 countries considering the total cases per million, total deaths per million, population over the age of 65, Gross Domestic Product (GDP) per capita, and hospital beds per 100k obtained from the Humanitarian Data Exchange (HDX) website for the dates of 15 May 2020 and 23 January 2021. Partition coefficient, partition entropy, modified partition coefficient, silhouette, fuzzy silhouette, and Xie and Beni index were used to determine the optimal number of clusters a the optimal number of clusters was found to be 4. Thus, the countries were grouped into 4 clusters for both datasets. According to the results of the analysis, the similarities among the countries were evaluated by comparing their figures for both dates during the pandemic.

Keywords

• COVID-19
• countries
• clustering
• similarities

References

1. 1. Escalera-Antezana JP, Lizon-Ferrufino NF, Maldonado-Alanoca A, et al. Clinical features of the first cases and a cluster of Coronavirus Disease 2019 (COVID-19) in Bolivia imported from Italy and Spain. Travel medicine and infectious disease 2020; 35, 101653.
2. 2. Wang C, Horby PW, Hayden FG, et al. A novel coronavirus outbreak of global health concern. The Lancet 2020; 395(10223):470–3.
3. 3. Emami A, Javanmardi F, Pirbonyeh N, et al. Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Archives of academic emergency medicine 2020; 8(1).
4. 4. https://github.com/datasets/covid-19 (May 13, 2020).
5. 5. Yang S, Cao P, Du P, et al. Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis. Annals of translational medicine 2020; 8(4).
6. 6. Fontanet A, Tondeur L, Madec Y, et al. Cluster of COVID-19 in northern France: A retrospective closed cohort study. medRxiv 2020.
7. 7. Chang TH, Wu JL, Chang LY. Clinical characteristics and diagnostic challenges of pediatric COVID-19: A systematic review and meta-analysis. Journal of the Formosan Medical Association 2020; 119(5), 982-989.
8. 8. Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E., et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel medicine and infectious disease 2020;101623.

9. 9. Roosa K, Lee Y, Luo R, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling 2020;5:256-263.
10. 10. Jia J, Hu X, Yang F, et al. Epidemiological characteristics on the clustering nature of COVID-19 in Qingdao City, 2020: a descriptive analysis. Disaster Medicine and Public Health Preparedness 2020; 1-17.
11. 11. Gupta S, Shankar R. Estimating the number of COVID-19 infections in Indian hot-spots using fatality data. arXiv preprint arXiv 2020;2004.04025.
12. 12. Liu D, Clemente L, Poirier C, et al. A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. arXiv preprint arXiv 2020; 2004.04019.
13. 13. Jung SM, Akhmetzhanov AR, Hayashi K, et al. Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. Journal of clinical medicine 2020; 9(2): 523.
14. 14. Anastassopoulou C, Russo L, Tsakris A, et al. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PloS one 2020; 15(3): e0230405.
15. 15. Randhawa GS, Soltysiak MP, El Roz H, et al. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos one 2020;15(4):e0232391.
16. 16. Ghosal S, Sengupta S, Majumder M, et al. Prediction of the number of deaths in India due to SARS-CoV-2 at 5–6 weeks. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020.
17. 17. Vaishya R, Javaid M, Khan IH, et al. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020;14(4):337-339.
18. 18. Sabzi A, Farjami Y, ZiHayat M. An improved fuzzy k-medoids clustering algorithm with optimized number of clusters. In 2011 11th International Conference on Hybrid Intelligent Systems (HIS) 2011; 206-210.
19. 19. https://data.humdata.org/dataset/total-covid-19-tests-performed-by-country (May 15, 2020, Jan 23, 2021).
20. 20. Park HS, Jun CH. A simple and fast algorithm for K-medoids clustering. Expert systems with applications 2009; 36(2):3336-3341.
21. 21. Levis ND. Applied Predictive Modeling Techniques in R 2015. ISBN-13: 978-1517516796.