Unraveling spatial patterns of COVID‐19 in Italy: Global forces and local economic drivers

Abstract

The article investigates the spatial patterns of the COVID-19 infection in Italy and its determinants from March 9 to June 15, 2020, a time interval covering the so called 'first wave' of COVID pandemics in Europe. The results, based on negative binomial regressions and linear spatial models, confirm the importance of multiple factors that positively correlate with the number of recorded cases during the first wave of the pandemic. Economic forces, including urban agglomeration, industrial districts, concentration of large companies (both before and after the beginning of the 'lockdown') and a North-South gradient are the most significant predictors of the strength of COVID-19 infection. These effects are statistically more robust in the spatial models than in the a-spatial ones. We interpretate our results in the light of pitfalls related to data reliability, and we discuss policy implications and possible avenues for future research.

Place-based factors affecting COVID-19 incidences in Turkey

“…In addition, most of the studies focused on international mobility between countries, and underlined the positive correlation between air traffic and COVID-19. In terms of the socioeconomic perspective, while GDP per capita as a global variable impacts the dissemination of the virus in terms of income level (Curtini and Salvati 2021 ), Gibson and Rush ( 2020 ) evaluated the household size under the scope of social distance issues. They claim that the high density in informal settlements could increase vulnerability to the virus.…”

“…In terms of economic factors, Curtini and Salvati ( 2021 ) investigated the spatial distribution of COVID-19 in Italy through selected global forces and economic drivers using regression models and local spatial analysis. The results show that urban agglomerations, including large-scale industries and companies, significantly affect COVID-19 incidence in different regions.…”

“…Some researchers have also focused on the relationship between health and social geography (De Kadt et al 2020 ; Gibson and Rush, 2020 ; Kuupiel et al 2020 ; Hierro and Maza 2022 ). Additionally, some have made interdisciplinary correlations between COVID-19 and social behavior (Allcot et al 2020 ; Kuchler et al 2020 ; Martinho 2021 ; Boumahdi et al 2021 ; Cutrini and Savati 2021 ; Florida and Mellander 2022 ).…”

Modelling geographical variations in fertility and population density of Italian and foreign populations at the local scale: a spatial Durbin approach for Italy (2002–2018)

“…The direct effect, 'represents the expected average change across all observations for the dependent variable in a particular region due to an increase of one unit for a specific explanatory variable in this region' (Golgher and Voss 2016, p. 185), while the indirect effect, 'represents the changes in the dependent variable of a particular region arising from a one-unit increase in an explanatory variable in another region' (Golgher and Voss 2016, p. 185). As usual in spatial regression analysis, we took an OLS model (aspatial regression model) as a benchmark and a tool of comparison of the results of the spatial model of regression (Benassi and Naccarato 2017;Burnham and Anderson 2002;Cutrini and Salvati, 2021;Yang et al 2015) using the Akaike Information Criterion, AIC (Akaike 1974) as the parameter of comparison (i.e. the model with the smaller AIC should be preferred).…”

Which factors influence mobility change during COVID‐19 in Germany? Evidence from German county data

“…Cutrini and Salvati ( 2021 ) analyze the spatial patterns during the first wave of the COVID‐19 pandemic in Italy. They discuss the relevance of multiple factors to explain the forces beyond the spatial dynamics of the pandemic such as airline networks, urbanization, economic sectors, and firm size for North and South Italy.…”