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The COVID-19 Epidemic Spreading Effects

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  • Chich-Ping Hu

    (Department of Urban Planning and Disaster Management, Ming Chuan University, No. 5 DeMing Rd., Gweishan District, Taoyuan City 333, Taiwan)

Abstract

Cities are hotbeds for the outbreak and spread of infectious diseases. In the process of urban development, frequent interpersonal interactions are conducive to the spread of viruses. After the outbreak of COVID-19 in Wuhan, China in 2019, it quickly spread to Europe, North America, and Asia. This paper collects data on the number of COVID-19-infected cases per 100,000 people in Taiwan from 1 January to 4 May 2022 and the researcher uses the spatial regression model to analyze the spatial effect of the COVID-19 epidemic. The results of the study find that the hot zones of COVID-19-infected cases per 100,000 people are distributed in Taipei City, New Taipei City, Keelung City, Yilan County, and Taoyuan City, and the cold zones are distributed in Changhua County, Yunlin County, Chiayi County, Chiayi City, Tainan City, and Kaohsiung City. There are three types of urban development indicators: density, urbanization, and transportation system and means of transport, all of which can significantly affect the spatial spread of COVID-19. There is a negative correlation between the area of the “urban planning” district, the “road area” per person, the current status of the urban planning district population “density”, and the number of infected cases of “COVID19”. There is a negative correlation between “urban planning”, “road area”, “urbanization”, and “density” of neighboring cities and “COVID19” in a certain city.

Suggested Citation

  • Chich-Ping Hu, 2022. "The COVID-19 Epidemic Spreading Effects," Sustainability, MDPI, vol. 14(15), pages 1-11, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9750-:d:882875
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    3. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    4. Meike Wolf, 2016. "Rethinking Urban Epidemiology: Natures, Networks and Materialities," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 40(5), pages 958-982, September.
    5. Bo Xu & Huaiyu Tian & Clive Eric Sabel & Bing Xu, 2019. "Impacts of Road Traffic Network and Socioeconomic Factors on the Diffusion of 2009 Pandemic Influenza A (H1N1) in Mainland China," IJERPH, MDPI, vol. 16(7), pages 1-14, April.
    6. Creighton Connolly & Roger Keil & S. Harris Ali, 2021. "Extended urbanisation and the spatialities of infectious disease: Demographic change, infrastructure and governance," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 245-263, February.
    7. Dirk Helbing & Lubos Buzna & Anders Johansson & Torsten Werner, 2005. "Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions," Transportation Science, INFORMS, vol. 39(1), pages 1-24, February.
    8. David M. Drukker & Peter Egger & Ingmar R. Prucha, 2013. "On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 686-733, August.
    9. Lung-fei Lee & Xiaodong Liu & Xu Lin, 2010. "Specification and estimation of social interaction models with network structures," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 145-176, July.
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