Urbanization and COVID‐19 incidence: A cross‐country investigation

Abstract

This paper investigates the determinants of the diffusion and intensity of the COVID‐19 at the country level, focusing on the role played by urban agglomeration, measured using three urban variables: percentage of the urban population, population density, and primacy. We estimate the influence of urban agglomeration on two outcome variables: cumulative number of cases and deaths per 100,000 inhabitants up to 31 December 2020, using both parametric and semiparametric models. We also explore possible spatial effects. The non‐linear effects of the urban variables on the intensity of the disease reveal non‐monotonous relationships, suggesting that it is the size of the urban system that is linked to a stronger incidence.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

“…Interestingly, there is currently no consensus around the effect of population density on the spread of the infection. While about one half of the studies point towards the positive link ( Gonzalez-Val & Sanz-Gracia, 2021 ; Kianfar, Mesgari, Mollalo, & Kaveh, 2022 ; Li, et al, 2021 ), the other half of them had shown no association ( Kumru, Yiğit, & Hayran, 2021 ; Teh, et al, 2021 ). Only a handful of studies have examined the role of measures of income level ( Leung, Sharma, Adithipyangkul, & Hosie, 2020 ; Li, et al, 2021 ), amount of education ( Li, et al, 2021 ; Bański, Mazur, & Kamińska, 2021 ), unemployment ( Jain & Singh, 2020 ) or household size ( El Mouhayyar, Jaber, Bergmann, Tighiouart, & Jaber, 2021 ) on infection rate.…”

“…The studied time spans by region of the world are presented as boxplots in the right panel of Figure 4 . There is large variation in the periods of analysis, ranging from 3 days ( Benitez, Courtemanche, & Yelowitz, 2020 ) to almost two years ( Gonzalez-Val & Sanz-Gracia, 2021 ). From visual inspection, the period of analysis covered by studies in, say, Europe, is shorter than those in North America and South America.…”

Lower COVID‐19 Incidence in Low‐Continentality West‐Coast Areas of Europe

“…Islam et al., 2021). For example, population density has been widely investigated, with diverging results (Paez, 2021); we chose urbanization to normalize our data because high urbanization has been shown to better explain high COVID‐19 incidence than population density (González‐Val & Sanz‐Gracia, 2021). Fourth, social vulnerability and mitigation measures may have a considerable influence on incidence (Kashem et al., 2021); particularly at the beginning of the pandemic, these measures were inconsistently applied across Europe and not well documented or easily obtained.…”

Necessity and challenges for the post-pandemic Hangzhou Asian Games: An interdisciplinary data science assessment

“…According to our results, at the continent level, the overall impact on Asian participating countries is lower than the world average in terms of Tokyo Olympic performance, which also implies that Asian countries may present a relatively more exciting post-pandemic event in the upcoming future at the Hangzhou Asian Games. The reason may lie in the fact that Asian sports powers (Wang et al, 2021 ), especially China (Chen et al, 2021 ), have taken more effective COVID-19 countermeasures, and the fact that most Asian countries are less urbanized than European and American countries (González-Val and Sanz-Gracia, 2022 ). Particularly, China, Japan, and South Korea are less affected by the COVID-19 pandemic than the world and Asian average levels no matter from the output of the Olympic regression model at the Tokyo time point or the forecasting of the Asian Games regression model at the second time point.…”