Original Article

Socio-Economic Factors Affecting the Regional Spread and Out-break of COVID-19 in China

Abstract

Background: This study investigated the impact of socio-economic factors on the spread and outbreak of COVID-19 based on Chinese data.

Methods: Cumulative confirmed cases were collected and divided into the First-stage cases cluster dominated by imported cases, and the Second-stage cases cluster dominated by secondary cases, according to the time of emergency state and Wuhan city lockdown. The linear regression was used for data analysis.

Results: A total of 12,877 cases in 30 provinces were analyzed in the study. The First-stage cases cluster included 675 cases and Second-stage cases cluster included 12,202 cases. The socio-economic factors were significantly associated with the cases (P<0.05). The GDP and proportion of population moving out of Wuhan were associate with the First-stage dominated by imported cases (b>0, P<0.05). The First-stage cases cluster, proportion of population moving out of Wuhan and urban population were associate with the Second-stage dominated by secondary cases (b>0, P<0.05).

Conclusion: Socio-economic factors had impacts on the spread and outbreak of COVID-19. The combination of different socio-economic indicators at different stages of the epidemic may help control the epidemic.

1. Huang C, Wang Y, Li X, et al (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395 (10223): P497-506.
2. World Health Organization (2020). Coronavirus disease (COVID-19) pandemic. Available from: https://www.who.int
3. Panovska-Griffiths J (2020). Can mathematical modelling solve the current Covid-19 crisis? BMC Public Health, 20 (1): 551.
4. Jones BA, Betson M, Pfeiffer DU (2017). Eco-social processes influencing infectious disease emergence and spread. Parasitology, 144 (1): 26-36.
5. Zhang T, Yin F, Zhou T, et al (2016). Multivariate time series analysis on the dynamic relationship between Class B notifiable diseases and gross domestic product (GDP) in China. Sci Rep, 6: 29.
6. Colizza V, Barrat A, Barthelemy M, et al (2007). Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS Med, 4 (1): e13.
7. Colizza V, Barrat A, Barthelemy M, et al (2006). The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci U S A, 103 (7): 2015-20.
8. Li Q, Guan X, Wu P, et al (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med, 382 (13): 1199-1207.
9. Yang Y, Lu Q, Liu M, et al (2020). Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China. medRxiv, DOI:10.1101/2020.02.10.20021675.
10. Wu T, Perrings C, Kinzig A, et al (2017). Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: A review. Ambio, 46 (1): 18-29.
11. Kim E, Erdos G, Huang S, et al (2020). Microneedle array delivered recombinant coronavirus vaccines: Immunogenicity and rapid translational development. EBioMedicine, 55: 102743.
12. Lu R, Zhao X, Li J, et al (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 395 (10224): 565-574.
13. Zhang B, Liu S, Tan T, et al (2020). Treatment with convalescent plasma for critically ill patients with SARS-CoV-2 infection. Chest, 158 (1): e9-e13.
14. Antoniou C, Yannis G, Papadimitriou E, et al (2016). Relating traffic fatalities to GDP in Europe on the long term. Accident Analysis & Prevention, 92: 89-96.
15. Dadgar I, Norström T (2017). Short-term and long-term effects of GDP on traffic deaths in 18 OECD countries, 1960-2011. J Epidemiol Community Health, 71 (2): 146-53.
16. Lu Y, Coops NC (2018). Bright lights, big city: Causal effects of population and GDP on urban brightness. PLoS One, 13 (7): e0199545.
17. Brower JL (2018). The Threat and Response to Infectious Diseases (Revised). Microb Ecol, 76 (1): 19-36.
18. Xiao JP, He JF, Deng AP, et al (2016). Characterizing a large outbreak of dengue fever in Guangdong Province, China. Infect Dis Poverty, 5: 44.
19. Li Z, Yin W, Clements A, et al (2012). Spatiotemporal analysis of indigenous and imported dengue fever cases in Guangdong province, China. BMC Infectious Diseases, 12: 132.
20. He ZH, Song T, Huang Q, et al (2020). Exploration and application of rapid risk assessment method in prevention and control of COVID-19 in urban areas: a case study based on data of Wenzhou. South China Journal of Preventive Medicine, 46(2):101-105.
21. Qian GQ, Yang NB, Ding F, et al (2020). Epidemiologic and Clinical Characteristics of 91 Hospitalized Patients with COVID-19 in Zhejiang, China: A retrospective, multi-centre case series. QJM, 113 (7): 474-481.
Files
IssueVol 50 No 7 (2021) QRcode
SectionOriginal Article(s)
Published2021-07-01
DOI https://doi.org/10.18502/ijph.v50i7.6620
Keywords
Socio-economic COVID-19 Epidemic Spread Outbreak China

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Yang L, Qi C, Yang Z, Shang L, Xie G, Wang R, Sun L, Xu M, Yang W, Chung MC. Socio-Economic Factors Affecting the Regional Spread and Out-break of COVID-19 in China. Iran J Public Health. 50(7):1324-1333.