A Microeconomic Analysis of the COVID-19 Distribution in Turkey

Yıl 2020, , 11 - 25, 28.12.2020

Yiğit Aydoğan

https://doi.org/10.33399/biibfad.759410

Cited By: 2

Öz

Daha büyük olan şehirler Türkiye'de COVID-19 pandemisinin etkisini artırmamaktadır. Türkiye'deki şehirler üzerine yapılan çalışmaya göre Gibrat Yasası geçerlidir ve salgın bireyler arasında şehrin büyüklüğüyle orantılı olarak yayılmaktadır. Pandeminin yayılma hızı şehir büyüklüğüyle birlikte artmamaktadır. COVID-19 vakaları ülkede log-normal dağılım göstermektedir. Şehirlerdeki 0-19 yaş aralığındakilerin nüfusa oranı raporlanan vaka sayıları üzerine negatif etkiye sahipken, 40-59 yaş grubu en fazla pozitif etkiye sahiptir. COVID-19 kaynaklı ölümlerin dağılımı da şehir büyüklüğüyle orantılı olup üstel ve normal dağılımlarla temsil edilebilmektedir.

Anahtar Kelimeler

Covid-19, Gibrat Yasası, Orantılı Etki Yasası, Şehir Büyüklük Dağılımı

A Microeconomic Analysis of the COVID-19 Distribution in Turkey

Öz

Larger cities do not amplify the COVID-19 pandemic in Turkey. Reports from Turkish cities provide evidence that the Gibrat’s Law holds and the infection grows among population in proportion to the city sizes. Growth of the pandemic is not faster in larger cities. COVID-19 cases are lognormally distributed throughout the country. While the 0-19 age group of the society is associated with a negative impact on the reported cases, 40-59 group has the most additive effect. Distribution of the reported deaths from COVID-19 does not grow in proportion to the city size, and may well be approximated by both exponential and normal distributions.

Anahtar Kelimeler

Covid-19, Gibrat’s Law, Law of the Proportionate Effect, City Size Distribution

Kaynakça

• Anatolian Agency. Retrieved on 04-08-2020. https://www.aa.com.tr/tr/koronavirus/turkiyenin-il-il-kovid-19-vaka-haritasi/1788776
• Barlow, N. S., & Weinstein, S. J. (2020). Accurate closed-form solution of the SIR epidemic model. Physica D: Nonlinear Phenomena, 132540.
• Bekiros, S., & Kouloumpou, D. (2020). SBDiEM: A new mathematical model of infectious disease dynamics. Chaos, Solitons & Fractals, 109828.
• Bernoulli, D. (1760). Essai d'une nouvelle analyse de la mortalité causée par la petite vérole, et des avantages de l'inoculation pour la prévenir. Histoire de l'Acad., Roy. Sci.(Paris) avec Mem, 1-45.
• Blasius, B. (2020). Power-law distribution in the number of confirmed COVID-19 cases. arXiv preprint arXiv:2004.00940.
• Davis, W. H., & Lappin, R. C. (1923). Mortality Rates 1910-1920 with population of the Federal Censuses of 1910 and 1920 and Intercensal Estimates of Population. US Government Printing Office.
• Dowd, J. B., Rotondi, V., Adriano, L., Brazel, D. M., Block, P., Ding, X., ... & Mills, M. C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. medRxiv. https://doi.org/10.1101/2020.03.15.20036293.
• Eggo, R. M., Cauchemez, S., & Ferguson, N. M. (2011). Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. Journal of the Royal Society Interface, 8(55), 233-243. http://doi.org/10.1098/rsif.2010.0216
• Gibrat, R. (1931). Les Inegalites Economiques. Paris: Libraire du Recueil Sirey.
• Heroy, S. (2020). Metropolitan-scale COVID-19 outbreaks: How similar are they? arXiv preprint arXiv:2004.01248.
• Lee, K. B., Han, S., & Jeong, Y. (2020). COVID-19, flattening the curve, and Benford’s law. Physica A: Statistical Mechanics and its Applications, 559, 125090.
• Lu, Z., Yu, Y., Chen, Y., Ren, G., Xu, C., Wang, S., & Yin, Z. (2020). A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects. arXiv preprint arXiv:2004.12308.
• Pareto, V. (1897). Cours d’Economie Politique, vol. 2. Paris.
• Park, M., Cook, A. R., Lim, J. T., Sun, Y., & Dickens, B. L. (2020). A systematic review of COVID-19 epidemiology based on current evidence. Journal of Clinical Medicine, 9(4), 967.
• Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:2002.06563.
• Prothero, J. (1986). Methodological aspects of scaling in biology. Journal of Theoretical Biology, 118(3), 259-286.
• Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., & Zheng, C. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet Infectious Diseases.
• Simon, H. A., & Bonini, C. P. (1958). The size distribution of business firms. The American Economic Review, 607-617.
• Stanley, M. H., Amaral, L. A., Buldyrev, S. V., Havlin, S., Leschhorn, H., Maass, P., .& Stanley, H. E. (1996). Scaling behaviour in the growth of companies. Nature, 379(6568), 804-806. https://doi.org/10.1038/379804a0
• Stier, A., Berman, M., & Bettencourt, L. (2020). COVID-19 attack rate increases with city size. Mansueto Institute for Urban Innovation Research Paper Forthcoming.
• Turkish Statistical Institute. (2020). Address Based Population Registration System [Data set]. https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
• West, G. B., Brown, J. H., & Enquist, B. J. (2000). Scaling in biology: patterns and processes, causes and consequences. Scaling in Biology, 87-112.
• Wood, J. G., Zamani, N., MacIntyre, C. R., & Becker, N. G. (2007). Effects of internal border control on spread of pandemic influenza. Emerging Infectious Diseases, 13(7), 1038. https://doi.org/10.3201/eid1307.060740
• World Health Organization. Retrieved on 04-08-2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen
• Wu, J.T., Leung, K., Bushman, M. et al. (2020). Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med (2020). https://doi.org/10.1038/s41591-020-0822-7
• Zhang, Y., Tian, H., Zhang, Y., & Chen, Y. (2020). Is the epidemic spread related to GDP? Visualizing the distribution of COVID-19 in Chinese Mainland. arXiv preprint arXiv:2004.04387.
• Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Cambridge: Addison-Wesley Press