The Impact of Inter-City Traffic Restriction on COVID-19 Transmission from Spatial Econometric Perspective
Abstract
The aim of this paper is to conduct a spatial correlation study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases released by the National Health and Construction Commission, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran’s I test shows that there is a positive spatial correlation between the 17 cities in the Hubei province. The result of Moran’s I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also associated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spatial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.
References
National Health Commission. Preventive measures against COVID-19 is class A. Available from: http://finance.china.com.cn/news/20200121/5178840.shtml [Accessed January 2020].
National Health Commission. WHO has named the pneumonia “COVID-19”, 2020. Available from: http://world.people.com.cn/n1/2020/021 2/c1002-31582421.html [Accessed February 2020].
World Health Organisation. Coronavirus disease (COVID-19) Pandemic. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 [Accessed April 2020].
Jin Y, et al. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res. 2020;7(1): 4. DOI: 10.1186/s40779-020-0233-6
Hubei Government. Wuhan COVID-19 prevention and control headquarters circular (No. 1); 2020. Available from: https://www.hubei.gov.cn/zhuanti/2020/gzxxgzbd/zxtb/202001/t20200123_2014402.shtml [Accessed January 2020].
Chen Z. Diffusion evolution mechanism of public health emergencies and collaborative emergency management mechanism. Ph.D. thesis. Southwest JiaoTong University; 2011.
Forster P, et al. Phylogenetic network analysis of SARS-CoV-2 genomes. Proceedings of the National Academy of the United States of America. 2020;117(17): 9241-9243. Available from: DOI: 10.1073/pnas.2004999117
Yan R, et al. Structure of dimeric full-length human ACE2 in complex with B0AT1. bioRxiv. 2020. DOI: 10.1101/2020.02.17.951848
Jung S, 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): 523-535. DOI: 10.3390/jcm9020523
Hendrickson C, Rilett LR. The COVID-19 pandemic and transportation engineering. Journal of Transportation Engineering Part A Systems, 2020;146(7): 01820001. DOI: 10.1061/JTEPBS.0000418
Mogaji E. Impact of COVID-19 on transportation in Lagos, Nigeria. Transportation Research Interdisciplinary Perspectives. 2020;6: 100154. DOI: 10.1016/j.trip.2020.100154
Bonful H, et al. Limiting spread of COVID-19 in Ghana: Compliance audit of selected transportation stations in the Greater Accra region of Ghana. PLoS ONE. 2020;15(9): e0238971-e0238971. DOI: 10.1371/journal.pone.0238971
Wang Y, Xu D, Shi L. Thoughts on urban transportation under the COVID-19 pandemic. Urban Transport of China. 2020;018(003): 88-92. DOI: 10.13813/j.cn11-5141/u.2020.0305
Tan L, Ma C. Choice behavior of commuters' rail transit mode during the COVID-19 pandemic based on logistic model. Journal of Traffic and Transportation Engineering (English Edition). 2020. DOI: 10.1016/j.jtte.2020.07.002
Li D, Zheng L. Transportation management and planning under the COVID-19 pandemic in Wuhan. Urban Transport of China. 2020;018(003): 11-16,50. DOI: 10.13813/j.cn11-5141/u.2020.0018
Epidemiology Group of Emergency Response Mechanism of New Coronavirus Pneumonia, Chinese Center for Disease Control and Prevention. Analysis of epidemiological characteristics of the 2019 Novel Coronavirus Pneumonia (COVID-19). Chinese Journal of Epidemiology.2020;41(2): 145-151. DOI: 10.3760/cma.j.issn.0254-6450.2020.02.003
Yang J. The spreading of infectious diseases with recurrent mobility of community population. Physica A. 2019;541(1): 123316. DOI: 10.1016/j.physa.2019.123316
Zhou H, Hao C, Zou X. The influence of migration on the burden of and response to infectious disease threats in China: A theoretically informed review. Journal of Public Administration. 2014;4(1): 4-28,182. DOI: 10.3969/j.issn.1674-2486.2014.04.001
Ren F, et al. The classification of population mobility and impact on infectious diseases. Soft Science of Health 2010;24(3): 272-275. DOI: 10.3969/j.issn.1003-2800.2010.03.030
Ma Z, Ma Z. System dynamics of simulation of spread of major infections disease—A case study on SARS. Journal of Southwest Jiaotong University. 2011;12(1): 132-136. DOI: 10.3969/j.issn.1009-4474.2011.04.026
Zhou J, et al. Unconventional prevention strategies for urban public transport in the COVID-19 epidemic: Taking Ningbo City as a case study. China Journal of Highway and Transport. 2020;33(12): 1-10. DOI: 10.13140/RG.2.2.20856.06405
Li W, et al. The impact of traffic exposure and land use patterns on the spread risk of COVID-19 at community level. China Journal of Highway and Transport. 2020;3(1): 1-17. Available from: http://kns.cnki.net/kcms/detail/61.1313.U.20200721.1636.002.html
Anselin L. Specification tests on the structure of interaction in spatial econometric models. Papers of the Regional Science Association.1984;54(1): 165-182. DOI: 10.1007/BF01940131
Chen Y, Zhang L. Research on the effect of technique choice on Chinese provincial economic development: Based on spatial autoregressive model with autoregressive disturbances panel data model. Journal of Huaqiao University (Philosophy & Social Sciences). 2017;3(2): 71-80. DOI: 10.3969/j.issn.1006-1398.2017.03.005
Hua W. The impact of Chinese trade and investment on West African economic growth: A spatial econometrics approach. Open Journal of Applied Sciences. 2020;10(1): 142-153. DOI: 10.4236/ojapps.2020.104012
Xu Y, Li A. The relationship between innovative human capital and interprovincial economic growth based on panel data model and spatial econometrics. Journal of Computational and Applied Mathematics. 2019;365: 112381. DOI: 10.1016/j.cam.2019.112381
Chica-Olmo J, González-Morales JG, Zafra-Gómez JL. Effects of location on Airbnb apartment pricing in Málaga. Tourism Management. 2020;77: 1-13. DOI: 10.1016/j.tourman.2019.103981
Ferrer Velasco R, Köthke M, Lippe M, Günter S. Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics. PLoS ONE. 2020;15(1): e0226830. DOI: 10.1371/journal.pone.0226830
Huang C. A study on floodplain inundation analysis and modelling method based on remote sensing and GIS. PhD thesis. East China Normal University; 2014.
Yang G, Zhou X, Wang T. Spatial autocorrelation analysis of schistosomiasis cases and oncomelania snail in three provinces of the lower reach of Yangtze River. Chinese Journal of Parasitology and Parasitic Diseases. 2002;20(1): 6-9. DOI: 10.3969/j.issn.1000-7423.2002.01.002
Wang Y, Wang C, Xia Y. Study of risk factors of endemic fluorosis in Jiangsu Province by spatial autoregressive model. Jiangsu Journal of Preventive Medicine. 2010;11: 106-109. DOI: 10.13668/j.issn.1006-9070.2016.06.13
Shen D, Ying L. Modeling the SARS epidemic in China Journal of Nanjing Xiaozhuang University. 2006;11(1): 62-66. DOI: 10.3969/j.issn.1009-7902.2006.06.015
Lin D, Cui Z, Zhao J. Detecting the correlation between atmosphere factors and tuberculosis incidence using spatial econometric model. Chongqingyixue. 2019;48(1): 2341-2344, 2348. DOI: 10.3969/j.issn.1671-8348.2019.14.001
Luo M, et al. Characteristic analysis of the urban integrated transportation in Wuhan City from the perspective of big data. Geospatial Information. 2020;18(5): 1-7. DOI: 10.3969/j.issn.1672-4623.2020.05.001
Zhang Y, Wang X, Bi Q. Travel-infected susceptibility based on transmission mechanism of COVID-19. Transport Research. 2020;6(01): 73-80. DOI: 10.16503/j.cnki.2095-9931.2020.01.010
Li J, et al. Estimation of the epidemic properties of the 2019 novel coronavirus: A mathematical modeling study. medRxiv. 2020. DOI: 10.2139/ssrn.3542150
Anselin L, Florax RJGM. New directions in spatial econometrics: Introduction. Berlin, Heidelberg: Springer; 1995. DOI: 10.1007/978-3-642-79877-1_1
Tobler WR. A computer movie simulating urban growth in the Detroit region. Economic Geography. 1970;46(3): 234-240. DOI: 10.2307/143141
Copyright (c) 2021 Xijin Lu, Changxi Ma
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
