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.
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References
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.1974.1100705
Anselin, L. (1995). Local Indicator of Spatial Association—LISA. Geographical Analysis, 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
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)02111-6
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/BF01581478
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-z
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/s12199-020-00904-2
Barro, R. J., & Sala-I-Martin, X. (1992). Convergence. Journal of Political Economy, 100, 223–251. https://doi.org/10.1086/261816
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.2020.109814
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.2020.114465
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.2020.110129
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/rsp3.12465
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.2020.109650
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.
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/s12879-020-05415-7
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.2020.11079
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.2020.100443
Haque, S. E., & Rahman, M. (2020). Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh. Environmental Science & Policy, 114, 253–255.
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.2021.127243
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.2022.1066396
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.
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.0233875
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.2021.111930
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.0238339
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-x
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/10.1080/00036846.2020.1827139
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/10.2307/2332142
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.2021.741
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/s41598-021-81935-9
Notari, A. (2021). Temperature dependence of COVID-19 transmission. Science of The Total Environment, 763, 144390. https://doi.org/10.1016/j.scitotenv.2020.144390
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.2020.109754
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.x
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.2019.29
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.0242398
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.14512
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.2020.0994
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.
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.2020.137212
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Antolini, F., Cesarini, S. & Truglia, F.G. The Role of Demographic and Environmental Factors in the Outbreak of COVID-19 Across Italian Provinces. Soc Indic Res 170, 893–910 (2023). https://doi.org/10.1007/s11205-023-03224-4
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DOI: https://doi.org/10.1007/s11205-023-03224-4