Explicando o contaxio da COVID-19 nos concellos portugueses usando modelos de autocorrelación espacial

Barra de artigos

pdf (English)
Publicado 22-05-2021

Contido principal do artigo

Paulo Mourao
Universidade do Minho-Escola de Economia e Gestão, Departamento de Economía, Campus de Gualtar, 4710-057 Braga, Portugal
Portugal
https://orcid.org/0000-0001-6046-645X
Ricardo Bento
Universidade de Trás-os-Montes e Alto Douro (UTAD), Centro de Estudos Transdisciplinares para o Desenvolvimento (CETRAD), Departamento de Engenharias, Quinta de Prados, 5000-801 Vila Real, Portugal
Portugal
http://orcid.org/0000-0003-4469-385X
Vol 30 No 1 (2021): Número Extraordinario. COVID-19 e os seus efectos económicos: Rupturas nas cadeas de valor e cambios nos patróns de consumo, Artigos, páxinas 1-12
DOI https://doi.org/10.15304/rge.30.1.6984
Recibido: 30-06-2020 Aceptado: 10-04-2021 Publicado: 22-05-2021
Copyright Como citar

Resumo

Este artigo investiga o patrón de contaxio da COVID-19 nos municipios portugueses entre o 23 de marzo e o 5 de abril (a fase exponencial). Recorremos a modelos de autocorrelación espacial para analizar como a veciñanza de espazos altamente infecciosos tamén contribuíu a infectar os municipios próximos. Utilizamos varios indicadores para o contaxio da COVID-19, desde o número de individuos infecciosos ata as taxas de infección. Como variables explicativas, ademais da proximidade espacial, tamén consideramos a densidade de poboación, a proporción da poboación de persoas maiores e a lonxitude do perímetro/fronteira municipal. Os nosos resultados indican que os municipios altamente densos tenden a contaminar áreas próximas. Os perímetros máis longos tamén mostraron un efecto positivo nos indicadores contaxiosos para un municipio determinado.

Palabras chave

Covid-19, Portugal, Contaxio espacial, Autocorrelación espacial
Citado por

Detalles do artigo

plugins.generic.funding.fundingData

Citas

Anselin, L. (1992). Spatial data analysis with GIS: An introduction to application in the social sciences. Technical Report 92-10. Santa Barbara, CA: National Center for Geographic Information and Analysis. Retrieved from: http://ncgia.ucsb.edu/technical-reports/PDF/92-10.pdf

Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2), 93-115. DOI: https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Anselin, L., & Hudak, S. (1992). Spatial econometrics in practice. A review of software options. Regional Science and Urban Economics, 22(3), 509-536. DOI: https://doi.org/10.1016/0166-0462(92)90042-Y

Anselin, L., & Rey, S. J. (2014). Modern spatial econometrics in practice: A guide to GeoDa, GeoDaSpace and PySAL. Chicago, IL: GeoDa Press.

Cavaco, C. (Coord.). (2016). Habitat III – National Report Portugal. Lisboa, Portugal: Direção Geral do Território. Retrieved from: http://habitat3.org/wp-content/uploads/PT_UN-HabitatIII_NationalReport_2016_08_04_EN.pdf

Chandra, S., Kassens-Noor, E., Kuljanin, G., & Vertalka, J. (2013). A geographic analysis of population density thresholds in the influenza pandemic of 1918-19. International Journal of Health Geographics, 12(1), 9. DOI: https://doi.org/10.1186/1476-072X-12-9

Cont, R., Kotlicki, A., & Xu, R. (2020). Modelling COVID-19 contagion: Risk assessment and targeted mitigation policies. MedRxiv. DOI: https://doi.org/10.1101/2020.08.26.20182477

Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., & CMMID COVID-19 working group, Eggo, R. M. (2020). Age-dependent effects in the transmission and control of COVID-19 epidemics. MedRxiv. DOI: https://doi.org/10.1101/2020.03.24.20043018

Elhorst, J. P. (2014). Spatial econometrics. From cross-sectional data to spatial panels. Springer Briefs in Regional Science. Berlin, Germany: Springer.

Gao, Q., Hu, Y., Dai, Z., Xiao, F., Wang, J., & Wu, J. (2020). The epidemiological characteristics of 2019 novel coronavirus diseases (COVID-19) in Jingmen, China. SSRN Electronic Journal, 2(8), 113-122. DOI: https://doi.org/10.2139/ssrn.3548755

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

Holko, A., Mędrek, M., Pastuszak, Z., & Phusavat, K. (2016). Epidemiological modeling with a population density map-based cellular automata simulation system. Expert Systems with Applications, 48, 1-8. DOI: https://doi.org/10.1016/j.eswa.2015.08.018

Hu, H., Nigmatulina, K., & Eckhoff, P. (2013). The scaling of contact rates with population density for the infectious disease models. Mathematical Biosciences, 244(2), 125-134. DOI: https://doi.org/10.1016/j.mbs.2013.04.013

INE. (2020). COVID-19: uma leitura do contexto demográfico e da expressão territorial da pandemia - Dados até 16 de dezembro. Lisboa, Portugal: Instituto Nacional de Estatística. Retrieved from: https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_destaques&DESTAQUESdest_boui=470299933&DESTAQUEStema=55481&DESTAQUESmodo=2

Kang, D., Choi, H., Kim, J.-H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases. DOI: https://doi.org/10.1016/J.IJID.2020.03.076

Krisztin, T., Piribauer, P., & Wögerer, M. (2020). The spatial econometrics of the coronavirus pandemic. Letters in Spatial and Resource Sciences, 13, 209-218. DOI: https://doi.org/10.1007/s12076-020-00254-1

Le Gallo, J., & Ertur, C. (2003). Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980-1995. Papers in Regional Science, 82, 175-201. Retrieved from: https://link.springer.com/article/10.1007/s101100300145

Li, R., Richmond, P., & Roehner, B. M. (2018). Effect of population density on epidemics. Physica A: Statistical Mechanics and Its Applications, 510, 713-724. DOI: https://doi.org/10.1016/j.physa.2018.07.025

Lima, I. D., Queiroz, J. W., Lacerda, H. G., Queiroz, P. V. S., Pontes, N. N., Barbosa, J. D. A., Martins, D. R., Weirahter, J. L., Pearson, R. C., Wilson, M. E., & Jeronimo, S. M. B. (2012). Leishmania infantum chagasi in Northeastern Brazil: Asymptomatic infection at the urban perimeter. American Journal of Tropical Medicine and Hygiene, 86(1), 99-107. DOI: https://doi.org/10.4269/ajtmh.2012.10-0492

Mansour, S., Al Kindi, A., Al-Said, A., Al-Said, A., & Atkinson, P. (2021). Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable Cities and Society, 65, 102627. DOI: https://doi.org/10.1016/j.scs.2020.102627

Mollalo, A., Vahedi, B., & Rivera, K. M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the Total Environment, 728, 138884. DOI: https://doi.org/10.1016/j.scitotenv.2020.138884

Neves, S. (14 Abril 2020). Covid-19: TVI retira reportagem em que dizia que Norte é mais afectado por ter população “menos educada”. Público. Retrieved from: https://www.publico.pt/2020/04/14/sociedade/noticia/covid19-tvi-retira-reportagem-dizia-norte-afectado-populacao-menos-educada-1912220

Oliva Denis, R. D., & Aldrey Vazquez, J. A. (2018). Patróns de distribución territorial da poboación estranxeira en Galicia, 1997-2017. Revista Galega de Economía, 27(2), 49-60. DOI: https://doi.org/10.15304/rge.27.2.5657

Páez, A., Lóez, F. A., Menezes, T., Cavalcanti, R., & Pitta, M. G. R. (2020). A spatio-temporal analysis of the environmental correlates of COVID-19 incidence in Spain. Geographical Analysis, 1-25. DOI: https://doi.org/10.1111/gean.12241

Ramírez-Aldana, R., Gómez-Verjan, J. C., & Bello-Chavolla, O. Y. (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PloS Neglected Tropical Diseases, 14(11). DOI: https://doi.org/10.1371/journal.pntd.0008875

Sannigrahi, S., Pilla, F., Basu, B., Sarkar Basu, A., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62, 102418. DOI: https://doi.org/10.1016/j.scs.2020.102418

Sayampanathan, A. A., Heng, C. S., Pin, P. H., Pang, J., Leong, T. Y., & Lee, V. J. (2021). Infectivity of asymptomatic versus symptomatic COVID-19. The Lancet, 397(10269), 93-94. DOI: https://doi.org/10.1016/S0140-6736(20)32651-9

Sarrias, M. (2020). Spatial models. Talca, Chile: Universidad de Talca.

Singh, R., & Adhikari, R. (2020). Age‐structured impact of social distancing on the COVID‐19 epidemic in India. arXiv:2003.12055 [q-bio.PE]. Retrieved from: https://arxiv.org/pdf/2003.12055.pdf

Sun, F., Matthews, S. A., Yang, T. C., & Hu, M. H. (2020). A spatial analysis of the COVID-19 period prevalence in US counties through June 28, 2020: Where geography matters? Annals of Epidemiology. 52, 54-59.e1. DOI: https://doi.org/10.1016/j.annepidem.2020.07.014

You, H., Wu, X., & Guo, X. (2020). Distribution of COVID-19 morbidity rate in association with social and economic factors in Wuhan, China: Implications for urban development. International Journal of Environmental Research and Public Health, 17(10), 3417. DOI: https://doi.org/10.3390/ijerph17103417

Zhang, C. H., & Schwartz, G. G. (2020). Spatial disparities in coronavirus incidence and mortality in the United States: An ecological analysis as of may 2020. The Journal of Rural Health, 36(3), 433-445. DOI: https://doi.org/10.1111/jrh.12476