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

Abstract

<jats:title>Abstract</jats:title> <jats:p>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 <jats:italic>ceteris paribus</jats:italic>, projected probabilities to be infected from coronavirus <jats:italic>rise</jats:italic> with higher population density and Gini coefficients and <jats:italic>drop</jats:italic> 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.</jats:p>

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

“…The net population density was positively and significantly associated with the virus infection. This is consistent with the results of previous studies that found a strong association between population density and infectious disease outbreak [ 8 , 27 ]. Interestingly, net employment density was not statistically significant in the model.…”

“…Hamidi et al [ 32 ] considered the vulnerable population, including the elderly, and the level of medical services and facilities, in addition to density, as the main factors affecting the spread of COVID-19 and mortality in the United States. Arbel et al [ 27 ] and Ehlert [ 28 ] reported that socioeconomic factors other than density are closely related to the COVID-19 fatality rate. Identifying the effect of social distancing on trip reduction in U.S. metropolitan areas, Hamidi & Zandiatashbar [ 59 ] employed demographic attributes, park accessibility, as well as density.…”

“…In a study of the 1918 Spanish Influenza (H1N1), Garrett [ 8 ] reported that there was a statistically significant relationship between mortality and population density and claimed that high-population-density counties served as a catalyst that accelerated the spread of the infectious disease. Arbel et al [ 27 ] assessed the influence of population density and socioeconomic factors on COVID-19 infection rates in Israel and found that the possibility of COVID-19 infection grew with population density levels. Ehlert [ 28 ] attempted to find the relationship between density and socio-economic variables vis-a-vis mortality rate.…”

Public Policy Lessons from the COVID-19 Outbreak: How to Deal with it in the Post-Pandemic World?

“…The estimated coefficients of population density are small and statistically insignificant, suggesting no meaningful link between population density and confirmed cases of Covid-19 at the global level. However, this result does not rule out the possibility of a positive association between population density and virus infection at the individual country or, for that matter, at a city level, as reported by Arbel et al (2020) for Israeli cities, among other studies.…”

Effects of income inequality on COVID-19 infections and deaths during the first wave of the pandemic: Evidence from European countries

“…For Brazil, there is evidence of a positive and significant correlation between income inequality and COVID-19 mortality (Demenech et al, 2020;Martines et al, 2021). Studies conducted in Germany, Israel and Spain have shown that infection rates in these countries have varied based on income inequality, with socioeconomically disadvantaged populations being more likely to be infected (AQuAS, 2020;Arbel et al, 2020;Wachtler et al, 2020). A comparative study of the 10 countries worldwide that have been the most affected by the pandemic used a multidimensional index, including income inequality, to show that the worse off a country is, the greater the impact of COVID-19 has been (Ruiz Estrada, 2020).…”