Do Population Density, Socio-Economic Ranking and Gini Index of Cities Influence Infection Rates from Coronavirus? Israel as a case Study

Abstract

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 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. The outcomes of our study show that ceteris paribus, projected probabilities to be infected from coronavirus rise with higher population density and Gini coefficients and drop with higher socio-economic ranking of the city. Moreover, when measured by identical units of standard deviations, the contribution of the socio-economic measure is the highest. Findings thus provide a tool to city and public health planners in an effort to address the spatial and socio-economic aspects of the pandemic. Compared with wealthier cities, poorer and denser cities should employ more pre-emptive measures to better enable the early identification of the incidence of COVID-19 in these cities. Finally, from a public health perspective, a densly populated city with a low socio-economic ranking and high income inequality requires immediate intervention in order to mitigate the dissemination of the virus.