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 rise in the median and average infection-population ratio for each level of population density; and 2) a moderate (a steep) rise 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.

COVID19 大流行引发了一场有趣的辩论，这直接关系到城市经济学的核心问题，即密集城市的优势和劣势。一方面，密集区域促进了更密集的人际互动，并可能导致更高的感染风险，这使它们成为大流行危机的潜在震中。另一方面，人口稠密的地区往往会提供更好的卫生和教育系统，这些系统可以更好地应对流行病，从而导致更高的康复率和更低的死亡率。本研究的目的是检验作为因变量的 COVID19 感染率（病例÷人口）与两个解释变量（人口密度和社会经济措施）之间的关系，时间范围为：2020 年 5 月 11 日和 1 月 19 日， 2021. 我们使用不同的方法来解决 COVID19 传播与人口密度之间的关系，方法是拟合抛物线模型而不是线性模型，同时控制社会经济指数。因此，我们通过允许非单调关系，更好地检查影响 COVID19 跨时空传播的因素。以色列提供了一个基于城市人口高度不均匀分布和多样化人口的有趣案例研究。分析结果展示了两种变化模式：1) 显着的 因此，我们通过允许非单调关系，更好地检查影响 COVID19 跨时空传播的因素。以色列提供了一个基于城市人口高度不均匀分布和多样化人口的有趣案例研究。分析结果展示了两种变化模式：1) 显着的 因此，我们通过允许非单调关系，更好地检查影响 COVID19 跨时空传播的因素。以色列提供了一个基于城市人口高度不均匀分布和多样化人口的有趣案例研究。分析结果展示了两种变化模式：1) 显着的每个人口密度水平的中位数和平均感染人口比率上升；2）适度（陡峭）上升2020 年 5 月 11 日（2021 年 1 月 19 日）人口密度为每平方公里 4000 至 20,000 人时人口密度增加的感染率。平均和中位数感染人口比率的显着上升可能归因于新的 COVID19 变种（即英国和南非变种）的结果，这反过来又加剧了病毒传播。感染率的陡峭斜率和感染人口比标准差的上升可能是由于空间分布不均匀造成的：以多种语言传播信息；全国不同地区的医疗基础设施水平不同；对社会疏远规则的不同程度的遵守；严格（有限）遵守社交距离规则。