https://doi.org/10.4081/gh.2022.1060

Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China

Vol. 17 No. s1 (2022): Special issue on COVID-19
Submitted: 25 November 2021
Accepted: 5 March 2022
Published: 18 March 2022
COVID-19, socio-economic development, coronavirus spread, structural equation models, China.
Abstract Views: 921
PDF: 271
HTML: 24
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Mao Guo
https://orcid.org/0000-0002-6407-4477
School of Geography and Ocean Science, Nanjing University, Nanjing; Collaborative Innovation Centre of South China Sea Studies, Nanjing University, China.
Lin Yang
yanglin@nju.edu.cn
School of Geography and Ocean Science, Nanjing University, Nanjing, China.
Feixue Shen
School of Geography and Ocean Science, Nanjing University, Nanjing, China.
Lei Zhang
School of Geography and Ocean Science, Nanjing University, Nanjing, China.
Anqi Li
School of Geography and Ocean Science, Nanjing University, Nanjing, China.
Yanyan Cai
School of Geography and Ocean Science, Nanjing University, Nanjing, China.
Chenghu Zhou
School of Geography and Ocean Science, Nanjing University, Nanjing; Collaborative Innovation Centre of South China Sea Studies, Nanjing University; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing, China.

Coronavirus disease 2019 (COVID-19) has strongly impacted society since it was first reported in mainland China in December 2020. Understanding its spread and consequence is crucial to pandemic control, yet difficult to achieve because we deal with a complex context of social environment and variable human behaviour. However, few efforts have been made to comprehensively analyse the socio-economic influences on viral spread and how it promotes the infection numbers in a region. Here we investigated the effect of socio-economic factors and found a strong linear relationship between the gross domestic product (GDP) and the cumulative number of confirmed COVID-19 cases with a high value of R2 (between 0.57 and 0.88). Structural equation models were constructed to further analyse the social-economic interaction mechanism of the spread of COVID-19. The results show that the total effect of GDP (0.87) on viral spread exceeds that of population influx (0.58) in the central cities of mainland China and that the spread mainly occurred through its interplay with other factors, such as socio-economic development. This evidence can be generalized as socio-economic factors can accelerate the spread of any infectious disease in a megacity environment. Thus, the world is in urgent need of a new plan to prepare for current and future pandemics.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Crossref
Scopus
Google Scholar
Europe PMC
Adda J, 2016. Economic activity and the spread of viral diseases: evidence from high frequency data. Q J Econ 131:891-941. DOI: https://doi.org/10.1093/qje/qjw005
Alexis A, 2003. Susceptible-infected-recovered (SIR) dynamics of COVID-19 and economic impact. arXiv:2003.11221.
Angelini M, Heuvelink G, Kempen B, Morrás H, 2016.Mapping the soils of an Argentine Pampas region using structural equation modelling. Geoderma 281:102-18. DOI: https://doi.org/10.1016/j.geoderma.2016.06.031
Bendavid E, Mulaney B, Sood N, Shah S, Bromley-Dulfano R, Lai C, Weissberg Z, Saavedra-Walker R, Tedrow J, Bogan A, Kupiec T, Eichner D, Gupta R, Ioannidis J, Bhattacharya J, 2021. COVID-19 antibody seroprevalence in Santa Clara County, California. Int J Epidemiol 50:410-9. DOI: https://doi.org/10.1093/ije/dyab010
Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, Schmidt, AL, Valensise CM, Scala A, Quattrociocchi W, Pammolli F, 2020. Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci 117:15530-5. DOI: https://doi.org/10.1073/pnas.2007658117
Chakraborti S, Maiti A, Pramanik S, Sannigrahi S, Pilla F, Banerjee A, Das DN, 2021. Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis. Sci Total Environ 765:142723. DOI: https://doi.org/10.1016/j.scitotenv.2020.142723
Chen Y, Lu P, Chang C, Liu T, 2020. A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE T Netw Sci Eng 7:3279-94. DOI: https://doi.org/10.1109/TNSE.2020.3024723
COVID-19 and Human Development: Assessing the Crisis, Envisioning the Recovery, 2020. Human development report 2020: Perspectives. Available from: http://hdr.undp.org/en/hdp-covid
Delikhoon M, Guzman MI, Nabizadeh R, NorouzianBaghani A, 2021. Modes of transmission of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and factors influencing on the airborne transmission: a review. Int J Environ Res Public Health 18:395. DOI: https://doi.org/10.3390/ijerph18020395
Dong E, Du H, Gardner L, 2020. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis 20:533-4. DOI: https://doi.org/10.1016/S1473-3099(20)30120-1
Enserink M, Kupferschmidt K, 2020. With COVID-19, modeling takes on life and death importance. Science 367:1414-5. DOI: https://doi.org/10.1126/science.367.6485.1414-b
Frakt A, 2018. How the economy affects health. JAMA 319:1187-8. DOI: https://doi.org/10.1001/jama.2018.1739
Ge Y, Zhang WB, Wang J, Liu M, Ren Z, Zhang X, Zhou C, Tian Z, 2021. Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China. BMC Public Health 21:604. DOI: https://doi.org/10.1186/s12889-021-10624-z
Ghasemi A, Zahediasl S, 2012. Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab 10:486-9. DOI: https://doi.org/10.5812/ijem.3505
Grace J, Keeley J, 2006. A structural equation model analysis of postfire plant diversity in California shrublands. Ecol Appl 16:503-14. DOI: https://doi.org/10.1890/1051-0761(2006)016[0503:ASEMAO]2.0.CO;2
Grace J, Anderson T, Seabloom E, Borer E, Adler P, Harpole W, Hautier Y, Hillebrand H, Lind E, Pärtel M, Bakker J, Buckley Y, Crawley M, Damschen E, Davies K, Fay P, Firn J, Gruner D, Hector A, Smith M, 2016. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529:390-3. DOI: https://doi.org/10.1038/nature16524
Guan D, Wang D, Hallegatte S, Davis SJ, Huo J, Li S, Bai Y, Lei T, Xue Q, Coffman D, Cheng D, Chen P, Liang X, Xu B, Lu X, Wang S, Hubacek K, Gong P, 2020. Global supply-chain effects of COVID-19 control measures. Nat Hum Behav 4:577-87. DOI: https://doi.org/10.1038/s41562-020-0896-8
Hao, J, Xu G, Luo L, Zhang Z, Yang H, Li H, 2020. Quantifying the relative contribution of natural and human factors to vegetation coverage variation in coastal wetlands in China. Catena 188:104429. DOI: https://doi.org/10.1016/j.catena.2019.104429
Hu L, Bentler P, 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equat Model Multidiscipl J 6:1-55. DOI: https://doi.org/10.1080/10705519909540118
Hutcheson G, 2011. Ordinary least-squares regression. In: L. Moutinho and G.D. Hutcheson (Eds.), The SAGE dictionary of quantitative management research. SAGE, pp. 224-228.
Jia J, Lu X, Yuan Y, Xu G, Jia J, Christakis N, 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582:389-94. DOI: https://doi.org/10.1038/s41586-020-2284-y
Lalwani S, Sahni G, Mewara B, Kumar R, 2020. Predicting optimal lockdown period with parametric approach using three-phase maturation SIRD model for COVID-19 pandemic. Chaos Solitons Fractals 138:109939. DOI: https://doi.org/10.1016/j.chaos.2020.109939
Li Q, Guan X, Wu P, Wang X, Zhou L, Wt L, 2020. Early transmission dynamics in Wuhan, China, of novel coronavirus infected pneumonia. N Engl J Med 382:1199-207.
Lloyd C, Chamberlain H, Kerr D, Yetman G, Pistolesi L, Stevens F, 2019. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3:108-39. DOI: https://doi.org/10.1080/20964471.2019.1625151
Ma T, Zhou Y, Zhou C, Haynie S, Pei T, Xu T, 2015. Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data. Remote Sens Environ 158:453-64. DOI: https://doi.org/10.1016/j.rse.2014.11.022
Markowitz S, Nesson E, Robinson J, 2019. The effects of employment on influenza rates. Econ Hum Biol 34:286-95. DOI: https://doi.org/10.1016/j.ehb.2019.04.004
Patton N, Lohse K, Godsey S, Crosby B, Seyfried M, 2018. Predicting soil thickness on soil mantled hillslopes. Nat Commun 9:3329. DOI: https://doi.org/10.1038/s41467-018-05743-y
Qiu Y, Chen X, Shi W, 2020. Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. J Popul Econ 9:1-46. DOI: https://doi.org/10.1101/2020.03.13.20035238
Sanderson E, Jaiteh M, Levy M, Redford K, Wannebo A, Woolmer G, 2002. The Human Footprint and the Last of the Wild. BioSci 52:891-904. DOI: https://doi.org/10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2
Schumacker R, Lomax G, 2004. A beginner’s guide to structural equation modeling. Psychology Press, New York. DOI: https://doi.org/10.4324/9781410610904
Sun Y, Hu X, Xie J, 2021. Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England. Sci Total Environ 758:143595. DOI: https://doi.org/10.1016/j.scitotenv.2020.143595
Sun Z, Zhang H, Yang Y, Wan H, Wang Y, 2020. Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Sci Total Environ 746:141347. DOI: https://doi.org/10.1016/j.scitotenv.2020.141347
Venter O, Sanderson E, Magrach A, Allan J, Beher J, Jones K, 2016. Global terrestrial Human Footprint maps for 1993 and 2009. Sci Data 3:160067. DOI: https://doi.org/10.1038/sdata.2016.67
Viboud C, Bjørnstad O, Smith D, Simonsen L, Miller M, Grenfell B, 2006. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312:447-51. DOI: https://doi.org/10.1126/science.1125237
Wei Y, Wang Y, Di Q, Choirat C, Wang Y, Koutrakis P, Zanobetti A, Dominici F, Schwartz J, 2019. Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population: time stratified, case crossover study. BMJ 367:l6258. DOI: https://doi.org/10.1136/bmj.l6258
Weinberger D, Krause T, Mølbak K, Cliff A, Briem H, Viboud C, Gottfredsson M, 2012. Influenza epidemics in Iceland over 9 decades: changes in timing and synchrony with the United States and Europe. Am J Epidemiol 176:649-55. DOI: https://doi.org/10.1093/aje/kws140
Wong C, 2002. Developing indicators to inform local economic development in England. Urban Stud 39:1833-63. DOI: https://doi.org/10.1080/0042098022000002984
Worldometer, 2020. Available from: https://www.worldometers.info/coronavirus/#repro Accessed: March 26.
Wu X, Yin J, Li C, Xiang H, Lv M, Guo Z, 2021. Natural and human environment interactively drive spread pattern of COVID-19: A city-level modeling study in China. Sci Total Environ 756:143343. DOI: https://doi.org/10.1016/j.scitotenv.2020.143343
Xia Y, Bjørnstad ON, Grenfell B, 2004. Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics. Am Nat 164:267-81. DOI: https://doi.org/10.1086/422341
Yan W, Nawaz M, Xu W, Jiang Z, Sun W, Lai J, 2020. Atmospheric pressure and population density as super-factors influencing the transmission of coronavirus disease 2019 (COVID-19). Sci Rep [Epub ahead of print]. DOI: https://doi.org/10.21203/rs.3.rs-93707/v2
Yang L, Shen F, Zhang L, Cai Y, Yi F, Zhou C, 2020. Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: A case study in Jiangsu Province, China. J Clean Prod 280. [Epub ahead of print]. DOI: https://doi.org/10.1016/j.jclepro.2020.124330
Zhang C, Chen C, Shen W, Tang F, Lei H, Xie Y, 2020. Impact of population movement on the spread of 2019-nCoV in China. Emerg Microbes Infect 9:988-90. DOI: https://doi.org/10.1080/22221751.2020.1760143
ZhangY, Tian H, Zhang Y, Chen Y, 2020. Is the epidemic spread related to GDP? Visualizing the distribution of COVID-19 in Chinese Mainland. arXiv:2004.04387.

How to Cite

Guo, M., Yang, L., Shen, F., Zhang, L., Li, A., Cai, Y., & Zhou, C. (2022). Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1060
Copyright (c) 2022 the Author(s) Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.