Explicando el contagio de la COVID-19 en los ayuntamientos portugueses usando modelos de autocorrelación espacial
Barra lateral del artículo
Contenido principal del artículo
Resumen
Este artículo investiga el patrón de contagio de la COVID-19 en los municipios portugueses entre el 23 de marzo y el 5 de abril (la fase exponencial). Recurrimos a modelos de autocorrelación espacial para analizar como la vecindaz a espacios altamente infecciosos también contribuyó a infectar a los municipios próximos. Utilizamos varios indicadores para lo contagio de la COVID-19, desde el número de individuos infecciosos hasta las tasas de infección. Como variables explicativas, además de la cercanía espacial, también consideramos la densidad de población, la proporción de la población de personas mayores y la longitud del perímetro/frontera municipal. Nuestros resultados indican que los municipios altamente densos tienden a contaminar áreas próximas. Los perímetros más largos también mostraron un efecto positivo en los indicadores contagiosos para un municipio determinado.
Palabras clave:
Citado por
Detalles del artículo
plugins.generic.funding.fundingData
-
Fundação para a Ciência e a Tecnologia
plugins.generic.funding.funderGrants UIDB/04011/2020;UIDB/03182/2020
Referencias
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