Arbel Yuval, Fialkoff Chaim, Kerner Amichai, Kerner Miryam
Sir Harry Solomon School of Economics and Management, Western Galilee College, Derech Hamichlala, P.O. Box 2125, Acre 2412101, Israel.
Institute of Urban and Regional Studies, Hebrew University of Jerusalem, Mt. Scopus, Jerusalem 9190501, Israel.
Cities. 2022 Jan;120:103400. doi: 10.1016/j.cities.2021.103400. Epub 2021 Jul 28.
The COVID19 pandemic motivated an interesting debate, which is related directly to core issues in urban economics, namely, the advantages and disadvantages of dense cities. On the one hand, compact areas facilitate more intensive human interaction and could lead to higher exposure to the infection, which make them the potential epicenter of the pandemic crisis. On the other hand, dense areas tend to provide superior health and educational systems, which are better prepared to handle pandemics, leading to higher recovery rates and lower mortality rates. The objective of the current study is to test the relationship between COVID19 infection rates (cases÷population) as the dependent variable, and two explanatory variables, population density and socio-economic measures, within two timeframes: May 11, 2020 and January 19, 2021. We use a different methodology to address the relationship between COVID19 spread and population density by fitting a parabolic, instead of a linear, model, while controlling socio-economic indices. We thus apply a better examination of the factors that shape the COVID19 spread across time and space by permitting a non-monotonic relationship. Israel provides an interesting case study based on a highly non-uniform distribution of urban population, and diversified populations. Results of the analyses demonstrate two patterns of change: 1) a significant in the median and average infection-population ratio for each level of population density; and 2) a moderate (a steep) in infection rates with increased population density on May 11, 2020 (January 19, 2021) for population densities of 4000 to 20,000 persons per square kilometer. The significant rise in the average and median infection-population ratios might be as attributed to the outcome of new COVID19 variants (i.e., the British and the South African mutants), which, in turn, intensify the virus spread. The steeper slope of infection rates and the rise in the standard deviation of the infection-population ratio may be explained by non-uniform spatial distribution of: dissemination of information in a variety of language; different levels of medical infrastructure in different parts of the country; varying levels of compliance to social distancing rules; and strict (limited) compliance to social distancing rules. The last factor of limited compliance might be the outcome of premature optimism due to extensive scope of the vaccination campaign in Israel, which is located in first place globally.
新冠疫情引发了一场有趣的辩论，这场辩论直接关乎城市经济学的核心问题，即人口密集城市的利弊。一方面，紧凑区域便于人们进行更密集的人际互动，这可能导致更高的感染风险，使其成为疫情危机的潜在中心。另一方面，人口密集地区往往拥有更优质的医疗和教育体系，更有能力应对疫情，从而实现更高的康复率和更低的死亡率。本研究的目的是在两个时间框架内（2020年5月11日和2021年1月19日），检验以新冠感染率（病例数÷人口数）为因变量，以及人口密度和社会经济指标这两个解释变量之间的关系。我们采用了一种不同的方法来处理新冠疫情传播与人口密度之间的关系，即拟合一个抛物线模型而非线性模型，同时控制社会经济指标。通过允许一种非单调关系，我们从而能更好地考察在不同时间和空间上影响新冠疫情传播的因素。以色列基于城市人口分布极不均衡以及人口多样化的情况，提供了一个有趣的案例研究。分析结果呈现出两种变化模式：1）在每个人口密度水平上，感染人口比率的中位数和平均值都有显著变化；2）在2020年5月11日（2021年1月19日），对于每平方公里4000至20000人的人口密度，感染率随着人口密度的增加而适度（急剧）上升。平均和中位数感染人口比率的显著上升可能归因于新冠病毒新变种（即英国变种和南非变种）的出现，这反过来加剧了病毒传播。感染率更陡峭的斜率以及感染人口比率标准差的上升，可能是由以下因素的空间分布不均导致的：信息以多种语言传播；该国不同地区医疗基础设施水平不同；对社交距离规则的遵守程度各异；以及对社交距离规则严格（有限）的遵守情况。最后一个有限遵守的因素可能是由于以色列大规模的疫苗接种运动处于全球领先地位，导致人们过早产生乐观情绪的结果。
