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Previous research demonstrates that ADHD is considered a risk factor for COVID-19. The current study attempts to investigate the relationships between infection, mortality and recovery rates from coronavirus and the prevalence of ADHD at the US statewide level. Based on information from 2011 regarding the prevalence of ADHD across the US by state, findings suggest that, while there are no correlations between ADHD and population size, infection and mortality rates from coronavirus, recovery rates (recovery-population ratio) rise with the prevalence of ADHD. Consequently, a possible explanation is that in coping with the disease, ADHD might provide an evolutionary advantage. An example of this phenomenon can be found in the gene that causes sickle-cell disease, which, as a non-dominant gene, helps cope with infection from malaria. If corroborated, research findings may support the conclusion that coronavirus limitations in special educational frameworks for ADHD would not be required or could be relaxed. JEL Codes: H75, I12

Coronavirus disease 2019 (COVID-19) is a declared global pandemic with multiple risk factors. Based on recent empirical studies, obesity is considered by several researchers as one of the serious risk factors for coronavirus-related complications. Yet, other scholars argue in favor of the existence of an obesity survival paradox. The objective of the current study is to analyze the potential relationships between different corona indicators and obesity on a statewide level. Since the United States is ranked as one of the OECD countries with a high level of overweight and obesity among its citizens-the majority of US states exceed the 30% benchmark of obese population-it is an especially interesting case study to explore this issue. In an attempt to estimate projected probabilities for infection by coronavirus and mortality rates as a function of obesity prevalence, the fractional logit regression is employed. Findings may support the counter-intuitive possibility of an obesity survival paradox. Consequently, ethical guidelines referring to priority in intubation and intensive care treatments should account for these complex relationships between obesity and corona. Both projected rates of infection and mortality drop with elevated prevalence of obesity. The reasons for these findings might be explained by several conditions such as elevated social distancing from more obese persons, increased metabolic reserves, more aggressive treatment, and unidentified factors that should be examined in future research.

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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.

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.