A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Disease Hotspot*; COVID-19/*epidemiology ; Urban Population/*statistics & numerical data; China ; Cities/epidemiology ; Humans ; Prevalence ; Regression Analysis

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.
(© 2022. The Author(s).)

Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382(8), 727–733. https://doi.org/10.1056/NEJMoa2001017 (2020). (PMID: 10.1056/NEJMoa2001017319789457092803)
Jebril, N. World Health Organization declared a pandemic public health menace: a systematic review of the coronavirus disease 2019 “COVID-19”. Available at SSRN 3566298, from  https://www.psychosocial.com/article/PR290311/25748/ (2020).
Nicola, M. et al. The socio-economic implications of the coronavirus pandemic (COVID-19): A review. Int. J. surg. 78, 185-193 (2020). (PMID: 32305533716275310.1016/j.ijsu.2020.04.018)
Guo, C. et al. Meteorological factors and COVID-19 incidence in 190 countries: An observational study. Sci. Total Environ. 757, 143783 (2021). (PMID: 3325705610.1016/j.scitotenv.2020.143783)
Fortaleza, C. M. C. B., Guimarães, R. B., De Almeida, G. B., Pronunciate, M. & Ferreira, C. P. Taking the inner route: Spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. Epidemiol. Infect. 148, E118 (2020). (PMID: 3259492610.1017/S095026882000134X)
Patel, A. & Shah, P. Rethinking slums, cities, and urban planning: Lessons from the Covid-19 pandemic. Cities Health. https://doi.org/10.1080/23748834.2020.1790252 (2020). (PMID: 10.1080/23748834.2020.1790252)
Cheshmehzangi, A. The City in Need: Resilience Enhancement and City Management in Disruptive Disease Outbreak Events (Springer, 2020). (PMID: 10.1007/978-981-15-5487-2)
Kang, M. et al. COVID-19 impact on city and region: What’s next after lockdown? Int. J. Urban Sci. 24(3), 297–315. https://doi.org/10.1080/12265934.2020.1803107 (2020). (PMID: 10.1080/12265934.2020.1803107)
Honey-Rosés, J. et al. The impact of COVID-19 on public space: An early review of the emerging questions-design, perceptions and inequities. Cities Health. https://doi.org/10.1080/23748834.2020.1780074 (2020). (PMID: 10.1080/23748834.2020.1780074)
Zhao, Z. K. Responding to the epidemic of COVID-2019 effectively, maintain economic and social development stably and healthy. China Economic Times (2020). 2020-02-17(004).
Xu, X. K. et al. The geographical destination distribution and effect of outflow population of Wuhan when the outbreak of the 2019-nCOV pneumonia. J. Univ. Electr. Sci. Technol. China 49, 324 (2020).
Cuifang, Q. Factors influencing the interprovincial spread and development of COVID-19: Data analysis based on 30 provinces and cities. J. Xi’an Jiaotong Univ. Med. Sci. 6, 757 (2020).
Accessed 4 Oct 2021. http://kns.cnki.net/kcms/detail/61.1399.r.20200417.1413.002.html .
Gordon, A. & Ying, G. The size distribution of Chinese cities. Reg. Sci. Urban Econom. 35, 756 (2005). (PMID: 10.1016/j.regsciurbeco.2005.01.003)
Fang, C., Wang, Z. & Ma, H. The theoretical cognition of the development law of China’s urban agglomeration and academic contribution. Acta Geogr. Sinica 73(4), 651–665. https://doi.org/10.11821/dlxb201804005 (2018). (PMID: 10.11821/dlxb201804005)
Shao, M., Tang, X., Zhang, Y. & Li, W. City clusters in China: Air and surface water pollution. Front. Ecol. Environ. 4(7), 353–361 (2006). (PMID: 10.1890/1540-9295(2006)004[0353:CCICAA]2.0.CO;2)
Zhao, L. China’s 13th five-year plan: Road map for social development. East Asian Policy 8(03), 19–32 (2016). (PMID: 10.1142/S179393051600026X)
Tao, Y. et al. Measuring urban environmental sustainability performance in China: A multi-scale comparison among different cities, urban clusters, and geographic regions. Cities 94, 200–210 (2019). (PMID: 10.1016/j.cities.2019.06.014)
Huang, J. C. & Chen, S. Q. Classification of China’s urban agglomerations. Prog. Geogr. 34(3), 290–301 (2015).
Pan, Y. et al. Associations of mental health and personal preventive measure compliance with exposure to COVID-19 information during work resumption following the COVID-19 outbreak in China: Cross-sectional survey study. J. Med. Internet Res. 22(10), e22596 (2020). (PMID: 32936776754687010.2196/22596)
Tian, H. et al. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368(6491), 638–642 (2020). (PMID: 32234804716438910.1126/science.abb6105)
Hemphill, J. F. Interpreting the magnitudes of correlation coefficients. Am. Psychol. 58, 78 (2003). (PMID: 1267482210.1037/0003-066X.58.1.78)
Seber, G. A. & Lee, A. J. Linear Regression Analysis Vol. 329 (Wiley, 2012).
IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp, 2019).
Sweet, S. A. & Grace-Martin, K. Data Analysis with SPSS Vol. 1 (Allyn & Bacon, 1999).
R. The R Project for Statistical Computing. Accessed 21 Oct 2021. http://www.r-project.org (2020).
Yicai. What Shapes Our Cities—2020 New First-Tier Cities Summit<什么塑造了我们的城市——2020新一线城市峰会 (2021). https://www.yicai.com/topic/100645942/ (Accessed 10 July 2021).
Hu, G. et al. Information disclosure during the COVID-19 epidemic in China: City-level observational study. J. Med. Internet Res. 22(8), e19572 (2020). (PMID: 32790640747370310.2196/19572)
Wu, X. et al. Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China. Sci. Total Environ. 756, 143343 (2021). (PMID: 3330207110.1016/j.scitotenv.2020.143343)
Hawkins, R. B., Charles, E. J. & Mehaffey, J. H. Socio-economic status and COVID-19-related cases and fatalities. Public Health 189, 129–134 (2020). (PMID: 3322759510.1016/j.puhe.2020.09.016)
Mamelund, S. E., Shelley-Egan, C. & Rogeberg, O. The association between socioeconomic status and pandemic influenza: Protocol for a systematic review and meta-analysis. Syst. Rev. 8(1), 1–6 (2019). (PMID: 10.1186/s13643-018-0931-2)
Takagi, H. et al. Meta-regression of COVID-19 prevalence/fatality on socioeconomic characteristics of data from top 50 US large cities. J. Med. Virol. 93, 595 (2020). (PMID: 3269735010.1002/jmv.26335)
Farzanegan, M. R., Gholipour, H. F., Feizi, M., Nunkoo, R. & Andargoli, A. E. International tourism and outbreak of coronavirus (COVID-19): A cross-country analysis. J. Travel Res. 60(3), 687–692 (2021). (PMID: 10.1177/0047287520931593)
Gössling, S., Scott, D. & Hall, C. M. Pandemics, tourism and global change: A rapid assessment of COVID-19. J. Sustain. Tour. 29(1), 1–20 (2020). (PMID: 10.1080/09669582.2020.1758708)
Chtourou, H. et al. Staying physically active during the quarantine and self-isolation period for controlling and mitigating the COVID-19 pandemic: A systematic overview of the literature. Front. Psychol. https://doi.org/10.3389/fpsyg.2020.01708 (2020). (PMID: 10.3389/fpsyg.2020.01708330134977466737)
Chen, M. et al. The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy. Inf. Fusion 64, 252–258 (2020). (PMID: 32834796740652010.1016/j.inffus.2020.08.002)
Shi, Z. & Fang, Y. Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China. MedRxiv. https://doi.org/10.1101/2020.03.15.20034199 (2020). (PMID: 10.1101/2020.03.15.20034199329090167418756)
Zheng, R., Xu, Y., Wang, W., Ning, G. & Bi, Y. Spatial transmission of COVID-19 via public and private transportation in China. Travel Med. Infect. Dis. 34, 101626 (2020). (PMID: 32184132711865110.1016/j.tmaid.2020.101626)
Ren, Y., Li, L. & Jia, Y. New method to reduce COVID-19 transmission—The need for medical air disinfection is now. J. Med. Syst. https://doi.org/10.1007/s10916-020-01585-8 (2020). (PMID: 10.1007/s10916-020-01585-8324408937241285)
Han, Y. et al. Spatial distribution characteristics of the COVID-19 pandemic in Beijing and its relationship with environmental factors. Sci. Total Environ. 761, 144257 (2021). (PMID: 3335234110.1016/j.scitotenv.2020.144257)
Tian, J. et al. Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: A multicentre, retrospective, cohort study. Lancet Oncol. 21(7), 893–903 (2020). (PMID: 32479790725991110.1016/S1470-2045(20)30309-0)
Şahin, M. Impact of weather on COVID-19 pandemic in Turkey. Sci. Total Environ. 728, 138810 (2020). (PMID: 32334158716988910.1016/j.scitotenv.2020.138810)
Jahangiri, M., Jahangiri, M. & Najafgholipour, M. The sensitivity and specificity analyses of ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) in different provinces of Iran. Sci. Total Environ. 728, 138872 (2020). (PMID: 32335407719472610.1016/j.scitotenv.2020.138872)
Gostin, L. O. Global Health Security: A Blueprint for the Future (Harvard University Press, 2021). (PMID: 10.4159/9780674269590)
Zhang, N. et al. Impact of intervention methods on COVID-19 transmission in Shenzhen. Build. Environ. 180, 107106 (2020). (PMID: 32834417733156410.1016/j.buildenv.2020.107106)
Chen, Y., Wang, Y., Wang, H., Hu, Z. & Hua, L. Controlling urban traffic-one of the useful methods to ensure safety in Wuhan based on COVID-19 outbreak. Saf. Sci. 131, 104938 (2020). (PMID: 32834520739204210.1016/j.ssci.2020.104938)
Prem, K. et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. Lancet Public Health 5(5), e261–e270 (2020). (PMID: 32220655715890510.1016/S2468-2667(20)30073-6)
Wu, J. T., Leung, K. & Leung, G. M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. The Lancet 395(10225), 689–697 (2020). (PMID: 10.1016/S0140-6736(20)30260-9)
Alonso-Villar, O. Urban agglomeration: Knowledge spillovers and product diversity. Ann. Reg. Sci. 36(4), 551–573 (2002). (PMID: 10.1007/s001680200090)
Allam, Z. & Jones, D. S. On the coronavirus (COVID-19) outbreak and the smart city network: Universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare 8, 46 (2020). (PMID: 715101810.3390/healthcare8010046)
Shi, W. et al. An extended weight kernel density estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China. Commun. Biol. 4, 126. https://doi.org/10.1038/s42003-021-01677-2 (2021). (PMID: 10.1038/s42003-021-01677-2334955097835364)
Chen, Z. L. et al. Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chin. Med. J. 133, 1044–1050. https://doi.org/10.1097/CM9.0000000000000782 (2020). (PMID: 10.1097/CM9.0000000000000782321186447147281)
Chen, R., Wang, N., Zhao, Y. & Zhou, Y. Complex network analysis of inter-provincial mobile population based on improved gravity model. China Popul. Resour. Environ. 24(10), 104–113 (2014).
Cheng, Q., Liu, Z., Cheng, G. & Huang, J. Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation. Sci. Rep. 10(1), 1–9. https://doi.org/10.1038/s41598-020-79063- (2020). (PMID: 10.1038/s41598-020-79063-)
Fang, C. The basic law of the formation and expansion in urban agglomerations. J. Geogr. Sci. 29(10), 1699–1712. https://doi.org/10.1007/s11442-019-1686-y (2019). (PMID: 10.1007/s11442-019-1686-y)
Gu, C. Study on urban agglomeration: Progress and prospects. Geogr. Res. 30(5), 771–784 (2011).
Mills, E. S. An aggregative model of resource allocation in a metropolitan area. Am. Econ. Rev. 57(2), 197–210 (1967).
Preen, M. China's City Clusters: The Plan to Develop 19 Super-Regions. China Briefing, August 14th 2018 (2018). https://www.china-briefing.com/news/chinas-city-clusters-plan-to-transform-into-19-super-regions/ (Accessed 02 October 2020).
Mofijur, M. et al. Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic. Sustain. Prod. Consumpt. 26, 343 (2020). (PMID: 10.1016/j.spc.2020.10.016)
Tranmer, M. & Elliot, M. Multiple linear regression. Cathie Marsh Centre Census Surv. Res. 5(5), 1–5 (2008).
Cartenì, A., Di Francesco, L. & Martino, M. How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study. Sci. Total Environ. 741, 140489 (2020). (PMID: 32599395731348410.1016/j.scitotenv.2020.140489)
Rath, S., Tripathy, A. & Tripathy, A. R. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes Metab. Syndr. 14(5), 1467–1474 (2020). (PMID: 32771920739522510.1016/j.dsx.2020.07.045)
Qin, L. et al. Gendered effects on inflammation reaction and outcome of COVID-19 patients in Wuhan. J. Med. Virol. 92(11), 2684–2692 (2020). (PMID: 3249729710.1002/jmv.26137)
Kang, D., Choi, H., Kim, J.-H. & Choi, J. Spatial epidemic dynamics of the COVID-19 outbreak in China. Int. J. Infect. Dis. 94, 96–102 (2020). (PMID: 32251789719459110.1016/j.ijid.2020.03.076)
COVID-19 Real-Time Tracking in China (2021). https://news.qq.com/zt2020/page/feiyan.htm#/ (Accessed 1 January 2021).
Baidu Map. Baidu Map Spring Festival Migration Big Data (2020). https://qianxi.baidu.com/2020/ (Accessed 05 October 2020).
Distance Database Between Cities in China (2021). http://www.china6636.com/ (Accessed 18 Jan 2021).
Okcu, D., Pektas, A. O. & Uyumaz, A. Creating a non-linear total sediment load formula using polynomial best subset regression model. J. Hydrol. 539, 662–673 (2016). (PMID: 10.1016/j.jhydrol.2016.04.069)
Mallows, C. L. Some comments on Cp. Technometrics 15, 661–675 (1973).
Gilmour, S. G. The interpretation of Mallows’s Cp-statistic. J. R. Stat. Soc. Ser. D (Stat.) 45(1), 49–56 (1996).
Boisbunon, A., Canu, S., Fourdrinier, D., Strawderman, W. & Wells, M. T. AIC, Cp and estimators of loss for elliptically symmetric distributions. Preprint at http://arXiv.org/1308.2766 (2013).
Scott, D. Tukey’s Ladder of Power (2020). http://onlinestatbook.com/2/transformations/tukey.html (Accessed 24 January 2021).
Tukey, J. W. Exploratory Data Analysis (Addison-Wesley, 1977).
Silhavy, R., Silhavy, P. & Prokopova, Z. Analysis and selection of a regression model for the use case points method using a stepwise approach. J. Syst. Softw. 125, 1–14 (2017). (PMID: 10.1016/j.jss.2016.11.029)
iiMedia Research. Data Analysis of China’s Tourism Industry Affected by Epidemic in 2020. Accessed 21 Oct 2021. https://baijiahao.baidu.com/s?id=1665182132422943708&wfr=spider&for=pc (2020).

71850410544 National Natural Science Foundation of China; 72050410358 National Natural Science Foundation of China; 71950410760 National Natural Science Foundation of China,China

Date Created: 20220205 Date Completed: 20220211 Latest Revision: 20221024

20221213

PMC8817036

10.1038/s41598-022-05859-8

35121784