Exploration on the evolutionary characteristics of COVID-19: a functional data view

LIU Shi-shi; ZHONG Rou; ZHANG Jing-xiao

doi:10.16462/j.cnki.zhjbkz.2021.04.002

Volume 25 Issue 4

May 2021

Turn off MathJax

Article Contents

Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(4): 376-383

LIU Shi-shi, ZHONG Rou, ZHANG Jing-xiao. Exploration on the evolutionary characteristics of COVID-19: a functional data view[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(4): 376-383. doi: 10.16462/j.cnki.zhjbkz.2021.04.002

Citation:

LIU Shi-shi, ZHONG Rou, ZHANG Jing-xiao. Exploration on the evolutionary characteristics of COVID-19: a functional data view[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(4): 376-383. doi: 10.16462/j.cnki.zhjbkz.2021.04.002

Citation:

PDF( 4237 KB)

Exploration on the evolutionary characteristics of COVID-19: a functional data view

doi: 10.16462/j.cnki.zhjbkz.2021.04.002

Center for Applied Statistics, Renmin University of China, School of Statistics, Renmin University of China, Beijing 100872, China

Funds:

National Statistical Science Research Major Project of National Bureau of Statistics 2020LD06

More Information

Corresponding author: ZHANG Jing-xiao, E-mail: zhjxiaoruc@163.com
Received Date: 2021-02-14
Rev Recd Date: 2021-03-22

Available Online: 2021-05-11

Publish Date: 2021-04-10

Abstract

Abstract

Objective We explored and analyzed the evolutionary characteristics of COVID-19 cases numbers in different regions over time since the outbreak of the epidemic, so as to compare kinds of prevention and control measures and gain experience for public health management. Methods From the perspective of functional data, we applied functional principal component analysis to catch the primary temporal characteristics in cases number data over several periods, and performed hierarchical clustering on daily cases curves from various countries based on functional principal component scores to find the similarities among states. Results The first 3 functional principal components were retained for cumulative confirmed, cured and death cases numbers of all countries over each time period, each reflecting certain variability pattern among data at different times. On each period, 5 clusters were obtained, and the countries in clusters has changed over time. Conclusions The global epidemic has been fluctuating that cases numbers repeatedly grew at the end of June, September and November in 2020, which shows that there has been no effective measures so far. Changes in the clustering results also indicated that centralized isolation and strict management of imported persons are still the relative fast and effective intervention measures.
- COVID-19,
- Cases number,
- Functional data,
- Principal component analysis,
- Clustering

FullText(HTML)

References(20)

References

[1]	Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. Lancet, 2020, 395(10223): 497-506. DOI: 10.1016/S0140-6736(20)30183-5.
[2]	Johns Hopkins University. COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)[DB/OL]. (2020-01-22)[2021-01-20]. https://github.com/CSSEGISandData/COVID-19.
[3]	Zarikas V, Poulopoulos SG, Gareiou Z, et al. Clustering analysis of countries using the COVID-19 cases dataset[J]. Data Brief, 2020, 31: 105787. DOI: 10.1016/j.dib.2020.105787.
[4]	Ramsay JO, Silverman BW. Functional data analysis[M]. NewYork: Springer-Verlag New York, 2005.
[5]	Hasenstab K, Scheffler A, Telesca D, et al. A multi-dimensional functional principal components analysis of EEG data[J]. Biometrics, 2017, 73(3): 999-1009. DOI: 10.1111/biom.12635.
[6]	Dong JJ, Wang L, Gill J, et al. Functional principal component analysis of glomerular filtration rate curves after kidney transplant[J]. Stat Methods Med Res, 2018, 27(12): 3785-3796. DOI: 10.1177/0962280217712088.
[7]	Backenroth D, Goldsmith J, Harran MD, et al. Modeling motor learning using heteroskedastic functional principal components analysis[J]. J Am Stat Assoc, 2018, 113(523): 1003-1015. DOI: 10.1080/01621459.2017.1379403.
[8]	Carroll C, Bhattacharjee S, Chen YQ, et al. Time dynamics of COVID-19[J]. Sci Rep, 2020, 10(1): 21040. DOI: 10.1038/s41598-020-77709-4.
[9]	Ward JH. Hierarchical grouping to optimize an objective function[J]. J Am Stat Assoc, 1963, 58(301): 236-244. DOI: 10.1080/01621459.1963.10500845.
[10]	高惠璇. 应用多元统计分析[M]. 北京: 北京大学出版社, 2004. Gao HX. Applied multi-variate statistical analysis[M]. Beijing: Peking University Press, 2004.
[11]	武汉市新冠肺炎疫情防控指挥部. 武汉市新型冠状病毒感染的肺炎疫情防控指挥部通告(第1号)[EB/OL]. (2020-01-23)[2021-03-22]. http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm. Wuhan COVID-19 Prevention and Control Headquarters. Wuhan COVID-19 Prevention and Control Headquarters announcement (No. 1)[EB/OL]. (2020-01-23)[2021-03-22]. http://www.gov.cn/xinwen/2020-01/23/content_5471751.htm.
[12]	疾病预防控制局. 新型冠状病毒感染的肺炎防控方案(第二版)[EB/OL]. (2020-01-22)[2021-03-22]. http://www.nhc.gov.cn/xcs/zhengcwj/202001/c67cfe29ecf1470e8c7fc47d3b751e88.shtml. Bureau of Disease Prevention and Control. Prevention and control plan for pneumonia caused by COVID-19 infection (second edition)[EB/OL]. (2020-01-22)[2021-03-22]. http://www.nhc.gov.cn/xcs/zhengcwj/202001/c67cfe29ecf1470e8c7fc47d3b751e88.shtml.
[13]	疾病预防控制局. 关于加强新型冠状病毒感染的肺炎疫情社区防控工作的通知. 肺炎机制发[2020]5号[EB/OL]. (2020-01-25)[2021-03-22]. http://www.nhc.gov.cn/jkj/s3577/202001/dd1e502534004a8d88b6a10f329a3369.shtml. Bureau of Disease Prevention and Control. Notice on strengthening community prevention and control of pneumonia epidemic caused by COVID-19 infection. Pneumonia Mechanism Publish[2020] N0.5[EB/OL]. (2020-01-25)[2021-03-22]. http://www.nhc.gov.cn/jkj/s3577/202001/dd1e502534004a8d88b6a10f329a3369.shtml.
[14]	Dighe A, Cattarino L, Cuomo-Dannenburg G, et al. Response to COVID-19 in South Korea and implications for lifting stringent interventions[J]. BMC Med, 2020, 18(1): 321. DOI: 10.1186/s12916-020-01791-8.
[15]	Supino M, d'Onofrio A, Luongo F, et al. The effects of containment measures in the Italian outbreak of COVID-19[J]. BMC Pub Health, 2020, 20(1): 1806. DOI: 10.1186/s12889-020-09913-w.
[16]	Salimi R, Gomar R, Heshmati B. The COVID-19 outbreak in Iran[J]. J Glob Heal, 2020, 10(1): 010365. DOI: 10.7189/jogh.10.010365.
[17]	Ministry of Health of Singapore. News Highlights. Additional border control measures to reduce further importation of COVID-19 cases[EB/OL]. (2020-03-22)[2021-03-22]. https://www.moh.gov.sg/news-highlights/details/additional-border-control-measures-to-reduce-further-importation-of-COVID-19-cases.
[18]	Ministry of Foreign Affairs of Japan. Consular Services. Border enforcement measures to prevent the spread of novel coronavirus (COVID-19)[EB/OL]. (2020-04-03)[2021-03-22]. https://www.mofa.go.jp/ca/fna/page4e_001053.html.
[19]	Wang H, Wang Z, Dong Y, et al. Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China[J]. Cell Discov, 2020, 6: 10. DOI: 10.1038/s41421-020-0148-0.
[20]	Fang Y, Nie Y, Penny M. Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: a data-driven analysis[J]. J Med Virol, 2020, 92(6): 645-659. DOI: 10.1002/jmv.25750.