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 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.