Do population density, socio-economic ranking and Gini Index of cities influence infection rates from coronavirus? Israel as a case study

Abstract

A prominent characteristic of the COVID-19 pandemic is the marked geographic variation in COVID-19 prevalence. The objective of the current study is to assess the influence of population density and socio-economic measures (socio-economic ranking and the Gini Index) across cities on coronavirus infection rates. Israel provides an interesting case study based on the highly non-uniform distribution of urban populations, the existence of one of the most densely populated cities in the world and diversified populations. Moreover, COVID19 challenges the consensus regarding compact planning design. Consequently, it is important to analyze the relationship between COVID19 spread and population density. The outcomes of our study show that ceteris paribus projected probabilities to be infected from coronavirus rise with population density from 1.6 to 2.72% up to a maximum of 5.17–5.238% for a population density of 20,282–20,542 persons per square kilometer (sq. km.). Above this benchmark, the anticipated infection rate drops up to 4.06–4.50%. Projected infection rates of 4.06–4.50% are equal in cities, towns and regional councils (Local Authorities) with the maximal population density of 26,510 and 11,979–13,343 persons per sq. km. A possible interpretation is that while denser cities facilitate human interactions, they also enable and promote improved health infrastructure. This, in turn, contributes to medical literacy, namely, elevated awareness to the benefits associated with compliance with hygienic practices (washing hands), social distancing rules and wearing masks. Findings may support compact planning design principles, namely, development of dense, mixed use, walkable and transit accessible community design in compact and polycentric regions. Indeed, city planners should weigh the costs and benefits of many risk factors, including the COVID19 pandemic.