Top

Social Indicators Research

Published in:

02-10-2023 | Original Research

The Role of Demographic and Environmental Factors in the Outbreak of COVID-19 Across Italian Provinces

Authors: Fabrizio Antolini, Samuele Cesarini, Francesco Giovanni Truglia

Published in: Social Indicators Research | Issue 3/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This research proposes an analysis of the spread of COVID-19 during the three waves at the provincial level in Italy. Specifically, this study examines the distribution of COVID-19 cases across Italian provinces to determine the potential existence of territorial clusters, which are measured by spatial autocorrelation among the provinces. Furthermore, this study examines whether a convergence process occurred in the contagion of the virus among the provinces, thereby estimating its relative speed. To this end, the β-convergence model—commonly applied in economic growth models—is employed in this study to consider the demographic and environmental variables present in each province. The initial level and growth of infections observed in a certain province are then related to the level of infections and their growth rate in all other Italian provinces. This econometric estimation is conducted for the first, second, and third waves of the pandemic between March 2020 and June 2021, using covariates such as population density, aging index, average temperature, pollution levels, and the duration of the waves. The hypothesis of COVID-19 convergence, verified in all three waves, recorded a rather different average speed, with an influence exerted by demographic and environmental variables. Additionally, the econometric analysis is integrated, considering spatial effects using the Spatial Autoregressive (SAR) and Spatial Error (SEM) models, after verifying the existence of autocorrelation of the residuals for the first and third waves. The results obtained in this second estimation only partially confirm the influence of demographic and environmental variables for the first and third waves. In the first wave—in addition to identifying any spillover effects among provinces—the effects are decomposed into direct, indirect, and total effects.

previous article National Culture and Financial Capability: A Global Perspective

next article Quality of Life and its Predictors among Persons with Visual Disabilities in Sri Lanka: Development of a New Measure

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Akaike, H. (1974). A new look at the statistical model identification. In IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705CrossRef

Anselin, L. (1995). Local Indicator of Spatial Association—LISA. Geographical Analysis, 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.xCrossRef

Anselin, L. (2013). Spatial Econometrics: Methods and Models. Netherlands: Springer.

Anselin, L., Bera, A. K., Florax, R., & Yoon, J. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77–104. https://doi.org/10.1016/0166-0462(95)02111-6CrossRef

Anselin, L., & Getis, A. (1992). Spatial statistical analysis and geographic information systems. The Annals of Regional Science, 26(1), 19–33. https://doi.org/10.1007/BF01581478CrossRef

Antolini, F., & Cesarini, S. (2021). Covid-19 and possible bias in statistical information. Statistica Applicata-Italian Journal of Applied Statistics, 1, 7–42.

Arbia, G. (2005). Convergence in Per-capita GDP across European Regions using Panel Data Models Extended to Spatial Autocorrelation Effects. ISAE Working Paper, No.51. SSRN: http://ssrn.com/abstract=936327.

Arbia, G., & Paelinck, J. H. (2003). Economic convergence or divergence? Modelling the interregional dynamics of EU regions 1985–1999. Geographical Systems, 5, 1–24. https://doi.org/10.1007/s10109-003-0114-zCrossRef

Azuma, K., Yanagi, U., Kagi, N., Kim, H., Masayuki, M., & Hayashi, M. (2020). Environmental factors involved in SARS-CoV-2 transmission: Effect and role of indoor environmental quality in the strategy for COVID-19 infection control. Environmental Health and Preventive Medicine, 25, 1–16. https://doi.org/10.1186/s12199-020-00904-2CrossRef

Barro, R. J., & Sala-I-Martin, X. (1992). Convergence. Journal of Political Economy, 100, 223–251. https://doi.org/10.1086/261816CrossRef

Baumol, W. J. (1986). Productivity growth, convergence, and welfare: What the long-run data show. American Economic Review, 76, 1072.

Bontempi, E., Vergalli, S., & Squazzoni, F. (2020). Understanding COVID-19 diffusion requires an interdisciplinary multi-dimensional approach. Environ Res., 188, 109814. https://doi.org/10.1016/j.envres.2020.109814CrossRef

Protezione Civile. (2020). pcm-dpc/COVID-19: COVID-19 Italia—Monitoraggio situazione. GitHub. Retrieved March 12, 2022, from https://github.com/pcm-dpc/COVID-19

Conticini, E., Frediani, B., & Caro, D. (2020). Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy? Environmental pollution, 261, 114465. https://doi.org/10.1016/j.envpol.2020.114465CrossRef

Copat, C., Cristaldi, A., Fiore, M., Grasso, A., Zuccarello, P., Signorelli, S. S., Conti, G., & Ferrante, M. (2020). The role of air pollution (PM and NO2) in COVID-19 spread and lethality: A systematic review. Environmental Research, 191, 110129. https://doi.org/10.1016/j.envres.2020.110129CrossRef

Cutrini, E., & Salvati, L. (2021). Unraveling spatial patterns of COVID-19 in Italy: Global forces and local economic drivers. Regional Science Policy and Practice, 13(S1), 73–108. https://doi.org/10.1111/rsp3.12465CrossRef

Domingo, J. L., & Rovira, J. (2020). Effects of air pollutants on the transmission and severity of respiratory viral infections. Environmental Research., 187, 109650. https://doi.org/10.1016/j.envres.2020.109650CrossRef

Ferrari, G., Jiménez, J. A. M., Jiménez, J. M., & Vargas, M. V. (2014). Principales tendencias de investigación en turismo. Septem Ediciones.

Ferrari, G., Jiménez, J. M., & Secondi, L. (2018). Tourists’ expenditure in Tuscany and its impact on the regional economic system. Journal of cleaner production, 171, 1437–1446.CrossRef

Giuliani, D., Dickson, M. M., Espa, G., & Santi, F. (2020). Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC infectious diseases, 20(1), 1–10. https://doi.org/10.1186/s12879-020-05415-7CrossRef

Goumenou, M., Sarigiannis, D., Tsatsakis, A., Anesti, O., Docea, A. O., Petrakis, D., & Calina, D. (2020). COVID-19 in Northern Italy: An integrative overview of factors possibly influencing the sharp increase of the outbreak (Review). Molecular Medicine Reports, 22, 20–32. https://doi.org/10.3892/mmr.2020.11079CrossRef

Guliyev, H. (2020). Determining the spatial effects of COVID-19 using the spatial panel data model. Spatial Statistics, 38, 100443. https://doi.org/10.1016/J.SPASTA.2020.100443CrossRef

Haque, S. E., & Rahman, M. (2020). Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh. Environmental Science & Policy, 114, 253–255.CrossRef

Hsiao, T. C., Cheng, P. C., Chi, K. H., Wanh, H. Y., Pan, S. Y., Kao, C., et al. (2022). Interactions of chemical components in ambient PM25 with influenza viruses. Journal of Hazardous Materials, 423, 127243. https://doi.org/10.1016/j.jhazmat.2021.127243CrossRef

Istituto Superiore della Sanità (ISS) (2020). Caratteristiche dei pazienti deceduti positivi all’infezione da SARS-CoV-2 in Italia, Report, In www.epicentro.iss.it, (Last access: November 2020).

Istituto Nazionale di Statistica (ISTAT). (2021). Datawarehouse Istat. Retrieved March 2022, from http://dati.istat.it/.

Lenzi, F. R., & Truglia, F. G. (2022). Territorial spillover of Covid-19 infections in Rome during the “second wave.” Frontiers in Sociology. https://doi.org/10.3389/fsoc.2022.1066396CrossRef

LeSage, J. P. (1999). The theory and practice of spatial econometrics. Toledo: University of Toledo.

LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. Boca Raton: Chapman and Hall/CRC.CrossRef

Livadiotis, G. (2020). Statistical analysis of the impact of environmental temperature on the exponential growth rate of cases infected by COVID-19. PLoS ONE, 15(5), 0233875. https://doi.org/10.1371/journal.pone.0233875CrossRef

Marques, M., & Domingo, J. L. (2021). Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences. Environmental Research, 203, 111930. https://doi.org/10.1016/j.envres.2021.111930CrossRef

Mecenas, P., Bastos, R. T. D. R. M., Vallinoto, A. C. R., & Normando, D. (2020). Effects of temperature and humidity on the spread of COVID-19: A systematic review. PLoS one. https://doi.org/10.1371/journal.pone.0238339CrossRef

Meliciani, V., & Peracchi, F. (2006). Convergence in per-capita GDP across European regions: a reappraisal. Empirical Economics, 31, 549–568. https://doi.org/10.1007/s00181-006-0053-xCrossRef

Moosa, I. A., & Khatatbeh, I. N. (2021). Robust and fragile determinants of the infection and case fatality rates of Covid-19: international cross-sectional evidence. Applied Economics, 53(11), 1225–1234. https://doi.org/10.1080/00036846.2020.1827139CrossRef

Moran, P. A. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society Series B Methodological, 10(2), 243–251.

Moran, P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17–33. https://doi.org/10.2307/2332142CrossRef

Murgia, N., Corsico, A. G., D’Amato, G., Maesano, C. N., Tozzi, A., & Annesi-Maesano, I. (2021). Do gene-environment interactions play a role in COVID-19 distribution? The case of Alpha-1 Antitrypsin, air pollution and COVID-19. Multidisciplinary respiratory medicine, 16(1), 741. https://doi.org/10.4081/mrm.2021.741CrossRef

Nor, N. S. M., Yip, C. W., Ibrahim, N., Jaafar, M. H., Rashid, Z. Z., Mustafa, N., et al. (2021). Particulate matter (PM2.5) as a potential SARS-CoV-2 carrier. Scientific Reports, 11(1), 2508. https://doi.org/10.1038/s41598-021-81935-9CrossRef

Notari, A. (2021). Temperature dependence of COVID-19 transmission. Science of The Total Environment, 763, 144390. https://doi.org/10.1016/j.scitotenv.2020.144390CrossRef

Setti, L., Passarini, F., De Gennaro, G., Barbieri, P., Perrone, M. G., Borelli, M., et al. (2020). SARS-Cov-2 RNA found on particulate matter of Bergamo in Northern Italy: first evidence. Environmental. Research., 188, 109754. https://doi.org/10.1016/j.envres.2020.109754CrossRef

Solow, R. M., & Swan, T. W. (1956). Economic growth and capital accumulation. Economic Record, 32, 334–361. https://doi.org/10.1111/j.1475-4932.1956.tb00434.xCrossRef

Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA) (2021). Qualità dell’aria. Retrieved March 2022, from https://annuario.isprambiente.it/sys_ind/macro/1

Truglia, F.G. (2021). La nuda città. Spillover territoriali dei contagi da Covid-19 a Roma nella “seconda ondata”. In La metropoli continua Storia e vita sociale del quadrante Sud di Roma (Materiali e Documenti n.78 ed., pp. 403-437). La Sapienza University Press.

Truglia, F. G. (2011). L’autocorrelazione spaziale e spazio-temporale. Struttura spaziale dell’astensionismo in Calabria, elezioni 1992–2008. Sociologia e ricerca sociale, 94, 111–129.

Truglia, F. G. (2019). Spatial analysis of economic and social determinants of vote: The case of the European parliament and constitutional referendum votes in Italy. Italian Political Science, 50(2), 173–190. https://doi.org/10.1017/ipo.2019.29CrossRef

Wang, J., Tang, K., Feng, K., & Lv, W. (2020). High temperature and high humidity reduce the transmission of COVID-19. Available at SSRN 3551767, 2020b.

Wong, D. W. S., & Li, Y. (2020). Spreading of COVID-19: Density matters. Plos One Journal. https://doi.org/10.1371/journal.pone.0242398CrossRef

Woodby, B., Arnold, M. M., & Valacchi, G. (2021). SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: what is the connection? Annals of the new York Academy of Sciences., 1486(1), 15–38. https://doi.org/10.1111/nyas.14512CrossRef

Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., & Song, Y. (2020). Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Internal Medicine, 180, 1–10. https://doi.org/10.1001/jamainternmed.2020.0994CrossRef

Wu, Y., Jing, W., Liu, J., Ma, Q., Yuan, J., Wang, Y., & Liu, M. (2020). Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Science of the Total Environment, 729, 139051.CrossRef

Wu, Z., Chen, Y., Han, Y., Ke, T., & Liu, Y. (2020). Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models. The Science of the Total Environment, 717, 137212. https://doi.org/10.1016/j.scitotenv.2020.137212CrossRef

Title: The Role of Demographic and Environmental Factors in the Outbreak of COVID-19 Across Italian Provinces
Authors: Fabrizio Antolini
Samuele Cesarini
Francesco Giovanni Truglia
Publication date: 02-10-2023
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 3/2023
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-023-03224-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2023

Does an Ageing Population Affect Crime Rates in the United States?

Cultural Intolerance, in Practice: Social Variation in Food and Drink Avoidances in Italy, 2003–2016

The Index Construction and Evaluation of Quality of Life in China and Its Spatial Mismatch with Subjective Well-Being: Evidence from a City-Level Perspective

Status Inconsistency, Gender, and Health in Korean Young Adults: Separating Educational and Skill Mismatch

Job Satisfaction and the ‘Great Resignation’: An Exploratory Machine Learning Analysis

National Culture and Financial Capability: A Global Perspective

Premium Partner