Regularidad global y variabilidad local de los patrones espacio temporales de la COVID 19 en Aragón (España)
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Con datos de casos confirmados de COVID 19 en Aragón (España), agregados en 123 Zonas Básicas de Salud durante 50 semanas consecutivas, se han identificado, medido y caracterizado los patrones espaciotemporales de la pandemia. Para este fin se han utilizado medidas de autocorrelación espacial y temporal de los datos obtenidas mediante la aplicación de procedimientos de estadística espacial (índices I global e Ii local de Moran). La incidencia espacial y temporal de la COVID 19 en Aragón no ha sido ni homogénea ni aleatoria, pues muestra cierta regularidad global y notable variabilidad local. Este modelo se puede explicar por un proceso de difusión espacial modificado por contagios a larga distancia y restringido por las medidas de control de la pandemia. La información obtenida es muy útil para tomar decisiones en materia de salud pública, relativas a la organización de los recursos sanitarios y a las determinaciones para la prevención y control de la pandemia.
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Bibliografía
Abler, R., Adams, J. S., & Gould, P. (1971). Spatial Organization. The geographer view of the world. Prentice-Hall.
Aleta, A., & Moreno, Y. (2020). Evaluation of the potential incidence of COVID-19 and effectiveness of containment measures in Spain: a data-driven approach. BMC Medicine, 18(1), 157. https://doi.org/10.1186/s12916-020-01619-5
Andrés López, G., Herrero Luque, D., & Martínez Arnaiz, M. (2021). Cartographies on COVID-19 and functional divisions of the territory: an analysis on the evolution of the pandemic based on Basic Health Areas (BHA) in Castile and Leon (Spain). Boletín de la Asociación de Geógrafos Españoles, (91). https://doi.org/10.21138/bage.3153
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin, L. (2020). Documentation / GeoDa on Github / GeoDa Workbook. https://geodacenter.github.io/documentation.html
Anselin, L. (2021). GeoDa (Tm) (1.20.). https://geodacenter.github.io/
Anselin, L., Lozano, N., & Koschinsky, J. (2006). Rate Transformations and Smoothing (Report). University of Illinois. https://es.scribd.com/document/78952443/Anselin-Smoothing-06.
Aragón Open Data (n.d.). https://opendata.aragon.es/datos/catalogo/dataset/publicaciones-y-anuncios-relacionados-con-el-coronavirus-en-aragon
Aràndiga, F., Baeza, A., Cordero-Carrión, I., Donat, R., Martí, M. C., Mulet, P., & Yáñez, D. F. (2020). A Spatial-Temporal Model for the Evolution of the COVID-19 Pandemic in Spain Including Mobility. Mathematics, 8(10), 1677. https://doi.org/10.3390/math8101677
Briz-Redón, Á., & Serrano-Aroca, Á. (2020). A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. The Science of the Total Environment, 728, 138811. https://doi.org/10.1016/j.scitotenv.2020.138811
Bryant, P., & Elofsson, A. (2020). Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries. PeerJ, 8, e9879. https://doi.org/10.7717/peerj.9879
Castro, M. C., Kim, S., Barberia, L., Ribeiro, A. F., Gurzenda, S., Ribeiro, K. B., Abbott, E., Blossom, J., Rache, B., & Singer, B. H. (2021). Spatiotemporal pattern of COVID-19 spread in Brazil. Science, 372(6544), 821-826. https://doi.org/10.1126/science.abh1558
Cos, O. de, Castillo, V., & Cantarero, D. (2020). Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans. International Journal of Environmental Research and Public Health, 17(22), 8468. https://doi.org/10.3390/ijerph17228468
Coura-Vital, W., Cardoso, D. T., Ker, F. T. de O., Magalhães, F. do C., Bezerra, J. M. T., Viegas, A. M., Morais, M. H. F., Bastos, L. S., Reis, I. A., Carneiro, M., & Barbosa, D. S. (2021). Spatiotemporal dynamics and risk estimates of COVID-19 epidemic in Minas Gerais State: analysis of an expanding process. Revista Do Instituto de Medicina Tropical de Sao Paulo, 63, e21. https://doi.org/10.1590/S1678-9946202163021
Cromley, E. K., & McLafferty, S. (2002). GIS and public health. Guilford Press. http://www.loc.gov/catdir/toc/fy031/2001054821.html
de Cos Guerra, O., Castillo Salcines, V., & Cantarero Prieto, D. (2021). Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic. Boletín de la Asociación de Geógrafos Españoles, (91). https://doi.org/10.21138/bage.3145
Elliott, P., & Wartenberg, D. (2004). Spatial Epidemiology: Current Approaches and Future Challenges. Environmental Health Perspectives, 112(9), 998–1006. https://doi.org/10.1289/ehp.6735
Fatima, M., O’Keefe, K. J., Wei, W., Arshad, S., & Gruebner, O. (2021). Geospatial Analysis of COVID-19: A Scoping Review. International Journal of Environmental Research and Public Health, 18(5), 2336. https://doi.org/10.3390/ijerph18052336
Fernández García, F., Herrera Arenas, D., & Fernández Bustamante, C. (2021). Dimensión temporal y territorial de la pandemia COVID-19 en Asturias. Boletín de la Asociación de Geógrafos Españoles, (91). https://doi.org/10.21138/bage.3147
Franch-Pardo, I., Desjardins, M. R., Barea-Navarro, I., & Cerdà, | Artemi. (2021). A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020. Transactions in GIS, 00, 1-49. https://doi.org/10.1111/tgis.12792
Franch-Pardo, I., Napoletano, B.M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment, 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033
Gaynor, T.S., & Wilson, M. E. (2020). Social Vulnerability and Equity: The Disproportionate Impact of COVID. Public Administration Review, 80(5), 832–838. https://doi.org/10.1111/puar.13264
Gobierno de Aragón (2020, June 22). Orden SAN/477/2020, de 22 de junio, por la que se adoptan medidas especiales en materia de salud pública para la contención del brote epidémico de la pandemia COVID-19 en las Comarcas de la Litera, Cinca Medio y Bajo Cinca. https://www.aragon.es/-/ordenes-del-departamento-de-sanidad-2020
Gobierno de Aragón (2020, June 23). Orden SAN/481/2020, de 23 de junio, por la que se adoptan medidas especiales en materia de salud pública para la contención del brote epidémico de la pandemia COVID- 19 en la Comarca de Bajo Aragón-Caspe/Baix Aragó-Casp. https://www.aragon.es/-/ordenes-del-departamento-de-sanidad-2020
Gobierno de Aragón. (n.d.-a). Aragón. Casos confirmados de COVID-19. https://datacovid.salud.aragon.es/covid/
Gobierno de Aragón. (n.d.-b). Aragón Open Data. Aragón: Datos y cifras sobre el Coronavirus. Https://Opendata.Aragon.Es/Datos/Catalogo/Dataset/Publicaciones-y-Anuncios-Relacionados-Con-El-Coronavirus-En-Aragon
Gobierno de Aragón. (n.d.-c). Órdenes del Departamento de Sanidad 2020-2022. https://www.aragon.es/-/ordenes-del-departamento-de-sanidad-2020
Gross, B., & Havlin, S. (2020). Epidemic spreading and control strategies in spatial modular network. Applied Network Science, 5(1), 95. https://doi.org/10.1007/s41109-020-00337-4
Hägerstrand, T. (1952). The propagation of innovation waves. Lund Studies in Geography, Serie B, 4, 1-20.
Huang, Z. (2021). Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective. ISPRS International Journal of Geo-Information, 10(8), 519. https://doi.org/10.3390/ijgi10080519
Instituto Geográfico de Aragón (IGEAR) (n.d.). https://idearagon.aragon.es/descargas.jsp
Jia, P., & Yang, S. (2020). Time to spatialise epidemiology in China. The Lancet Global Health, 8(6), e764-e765. https://doi.org/10.1016/S2214-109X(20)30120-0
Jiang, J., & Luo, L. (2020). Influence of population mobility on the novel coronavirus disease (COVID-19) epidemic: based on panel data from Hubei, China. Global Health Research and Policy, 5(1). https://doi.org/10.1186/s41256-020-00151-6
Kirby, R. S., Delmelle, E., & Eberth, J. M. (2017). Advances in spatial epidemiology and geographic information systems. Annals of Epidemiology, 27(1), 1-9. https://doi.org/10.1016/j.annepidem.2016.12.001
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481-1496.
Li, Y., Li, M., Rice, M., Zhang, H., Sha, D., Li, M., Su, Y., & Yang, C. (2021). The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective. International Journal of Environmental Research and Public Health, 18(3). https://doi.org/10.3390/ijerph18030996
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., ... Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet, 395(10224), 565–574. https://doi.org/10.1016/S0140-6736(20)30251-8
Meliker, J. R., & Sloan, C. D. (2011). Spatio-temporal epidemiology: Principles and opportunities. Spatial and Spatio-Temporal Epidemiology, 2(1), 1-9. https://doi.org/10.1016/j.sste.2010.10.001
Méndez, R. (2020). Sitiados por la pandemia. Del colapso a la reconstrucción: apuntes geográficos. Revives. http://revives.es/publicaciones/
Miramontes Carballada, Á., & Balsa-Barreiro, J. (2021). Territorial impact of the COVID-19 pandemic in Galicia (Spain): a geographical approach. Boletín de la Asociación de Geógrafos Españoles, (91). https://doi.org/10.21138/bage.3157
Mo, C., Tan, D., Mai, T., Bei, C., Qin, J., Pang, W., & Zhang, Z. (2020). An analysis of spatiotemporal pattern for COIVD-19 in China based on space-time cube. Journal of Medical Virology, 92(9), 1587-1595. https://doi.org/10.1002/jmv.25834
Moran, P. A. P. (1948). The Interpretation of Statistical Maps. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 243-251.
Odland, J. (2020). Sapatial Autocorrelation (G. I. Thrall, Ed.). WVU Research Repository. https://researchrepository.wvu.edu/rri-web-book/?utm_source=researchrepository.wvu.edu%2Frri-web-book%2F20&utm_medium=PDF&utm_campaign=PDFCoverPages
Ord, J.K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286-306. https://doi.org/10.1111/J.1538-4632.1995.TB00912.X
Perez-Bermejo, M., & Murillo-Llorente, M.T. (2020). The Fast Territorial Expansion of COVID-19 in Spain. Journal of Epidemiology, 30(5), 236. https://doi.org/10.2188/jea.JE20200123
Perles, M.-J., Sortino, J.F., & Mérida, M.F. (2021). The Neighborhood Contagion Focus as a Spatial Unit for Diagnosis and Epidemiological Action against COVID-19 Contagion in Urban Spaces: A Methodological Proposal for Its Detection and Delimitation. International Journal of Environmental Research and Public Health, 18(6), 3145. https://doi.org/10.3390/ijerph18063145
Roques, L., Bonnefon, O., Baudrot, V., Soubeyrand, S., & Berestycki, H. (2020). A parsimonious model for spatial transmission and heterogeneity in the COVID-19 propagation. Royal Society Open Science, 7(12). https://doi.org/10.1098/rsos.201382
Rosillo, N., Del-Águila-Mejía, J., Rojas-Benedicto, A., Guerrero-Vadillo, M., Peñuelas, M., Mazagatos, C., Segú-Tell, J., Ramis, R., & Gómez-Barroso, D. (2021). Real time surveillance of COVID-19 space and time clusters during the summer 2020 in Spain. BMC Public Health, 21(1), 961. https://doi.org/10.1186/s12889-021-10961-z
Salvador, C. E., Berg, M. K., Yu, Q., San Martin, A., & Kitayama, S. (2020). Relational Mobility Predicts Faster Spread of COVID-19: A 39-Country Study. Psychological Science, 31(10), 1236-1244. https://doi.org/10.1177/0956797620958118
Shi, W., Tong, C., Zhang, A., Wang, B., Shi, Z., Yao, Y., & Jia, P. (2021). An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China. Communications Biology, 4(1), 126. https://doi.org/10.1038/s42003-021-01677-2
Sigler, T., Mahmuda, S., Kimpton, A., Loginova, J., Wohland-Jakhar, P., Charles-Edwards, E., & Corcoran, J. (2021). The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population. Globalization and Health, 17, 56. https://doi.org/10.1186/s12992-021-00707-2
Souris, M. (2019). Épidémiologie et géographie, principes, méthodes et outils de l´analyse spatiales. ISTE Editions Ltd.
Souza, C.D.F. de, Paiva, J.P.S. de, Leal, T.C., Silva, L.F. da, & Santos, L.G. (2020). Spatiotemporal evolution of case fatality rates of COVID-19 in Brazil, 2020. Jornal brasileiro de pneumologia: publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia, 46(4), e20200208. https://doi.org/10.36416/1806-3756/e20200208
Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234-240.
Tobler, W. (1984). Applications of image processing techniques to map processing. In K. Brassel (Ed.), Proceedings of the international symposium on spatial data handling (pp. 140-144). Geograph. Inst., Abt. Kartographie/EDV.
Velasco, J.L. (2021, April 14). El “efecto autovía” o como las carreteras transmiten el virus por Aragón. Heraldo de Aragón. https://www.heraldo.es/noticias/aragon/2021/04/14/el-efecto-autovia-o-como-las-carreteras-transmiten-el-virus-por-aragon-1484576.html
Zhu, D., Ye, X., & Manson, S. (2021). Revealing the spatial shifting pattern of COVID-19 pandemic in the United States. Scientific Reports, 11(1), 8396. https://doi.org/10.1038/s41598-021-87902-8