Do COVID19 infection rates change over time and space? Population density and socio-economic measures as regressors.

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