Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic
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Minería de datos y patrones socio-espaciales de la COVID-19: claves de geoprevención para hacer frente a la pandemia
La perspectiva geográfica es esencial para afrontar la COVID-19. Este estudio se enmarca en el convenio de colaboración establecido por la Universidad de Cantabria, el Instituto de Investigación Sanitaria de Valdecilla (IDIVAL) y el Gobierno de Cantabria. El ámbito de estudio es el área urbana funcional de Santander y la investigación se desarrolla con perspectiva multiescalar. La principal fuente es el registro diario de microdatos de casos positivos COVID-19 y la metodología está basada en geo tecnologías de ESRI, y más concretamente en la herramienta SITAR (Sistema de Información Territorial de Acción Rápida) implementada por el equipo investigador. El principal objetivo de este estudio es contribuir al conocimiento de los patrones espaciales de la COVID-19 a nivel de vecindario con perspectiva espacio-temporal. Para conseguir este objetivo la investigación incorpora métodos de minería de datos (cubos 3D y análisis de puntos calientes emergentes) así como análisis geo-estadísticos exploratorios (Índice de Moran global, vecino más cercano y mínimos cuadrados ordinarios). Con relación a los resultados, el estudio identifica patrones espacio-tiempo diferenciados con significación estadística como puntos calientes y demuestra la coincidencia de elevada presencia de casos a nivel de edificio con vecindarios donde la función residencial está combinada con actividades económicas. En definitiva, avanzar en el conocimiento del comportamiento espacial del virus es estratégico para proponer claves de geoprevención, reducir la propagación y equilibrar las compensaciones entre los posibles beneficios para la salud y las cargas económicas que surgen de las intervenciones pandémicas.
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Bibliografía
Batista, F., & Poelman, H. (2016). Mapping Population Density in Functional Urban Areas. A Method to Downscale Population Statistics to Urban Atlas Polygons (JRC Technical Reports). European Commission. https://doi.org/10.2791/06831
Brizuela, N. G., García-Chan, N., Gutiérrez, H., & Chowell, G. (2021). Understanding the role of urban design in disease spreading. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476, 20200524. https://doi.org/10.1098/rspa.2020.0524
Brundson, C. A., Fotheringham, A. S., Charlton, M. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4). 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Brunsdon, C., Fotheringham, A. S., Charlton, M. (2002). Geographically weighted summary statistics — a framework for localised exploratory data analysis. Computers, Environment and Urban Systems, 26(6), 501-524. https://doi.org/10.1016/S0198-9715(01)00009-6
Campagna, M. (2020). Geographic Information and Covid-19 outbreak. Does the spatial dimension matter? Tema. Journal of Land Use, Mobility and Environment, special issue, 31-44. https://doi.org/10.6092/1970-9870/6850
Chirico, F., Sacco, A., Bragazzi, N. L., & Magnavita, N. (2020). Can air-conditioning systems contribute to the spread of SARS/MERS/COVID-19 infection? Insights from a rapid review of literature. International Journal of Environmental Research and Public Health, 17(17), 6052. https://doi.org/10.3390/ijerph17176052
De Cos, O., 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
De Cos, O., Castillo, V., & Cantarero, D. (2021). Differencing the risk of reiterative spatial incidence of COVID-19 using space-time 3D bins of geocoded daily cases. International Journal of Geo-Information, 10, 261. https://doi.org/10.3390/ijgi10040261
Eichler, N., Thornley, C., Swadi, T., Devine, T. Mackelnay, C., Sherwood, J., Brunton, C., Williamson, F., Freeman, J., Berger, S., Ren, X., Storey, M., de Ligt, J., & Geoghegan, J.L. (2021). Transmission of severe acute respiratory syndrome Coronavirus 2 during border quarantine and air travel, New Zealand (Aotearoa). Emerging infectious diseases, 27(5). https://doi.org/10.3201/eid2705.210514
Evans, P. J., & Evans, F. C. (1954). Distance to nearest neighbour as a measure of spatial relations in populations. Ecology, 35, 445-453.
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(2336). https://doi.org/10.3390/ijerph18052336
Franch-Pardo, I., Desjardins, M., Barea-Navarro, I., & Cerdà, A. (2021). A review of GIS methodologies to analyze the dynamics of COVD-19 in the second half of 2020. Transactions in GIS, 00, 1-49. https://doi.org/10.1111/tgis.12792
Greenhalgh, T., Jimenez, J. L., Prather, K.A., Tufekci, Z., Fisman, D., & Schooley, R. (2021, April 15). Ten scientific reasons in support or airborne transmission of SARS-Cov-2. The Lancet. https://doi.org/10.1016/S0140-6736(21)00869-2
Hamidi, S., Sabouri, S., & Ewing, R. (2020). Does Density Aggravate the COVID-19 Pandemic? Early Findings and Lessons for Planners. Journal of the American Planning Association, 86(4), 495-509. https://doi.org/10.1080/01944363.2020.1777891
Hernando, F. (2008). La seguridad en las ciudades: el nuevo enfoque de la geoprevención. Scripta Nova, 12, 270(14). http://www.ub.edu/geocrit/sn/sn-270/sn-270-14.htm
Huang, J., Kwan, M-P., Kan, Z., Wong, M. S., Tung Kwok, C. Y., & Yu, X. (2020). Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong. ISPRS International Journal of Geo-Information, 9, 624. https://doi.org/10.3390/ijgi9110624
Hwang, S. E., Chang, J. H., Oh, B., & Heo, J. (2021). Possible aerosol transmission of COVID-19 associated with an outbreak in an apartment in Seoul, South Korea, 2020. International Journal of Infectious Diseases, 104, 73-76. https://doi.org/10.1016/j.ijid.2020.12.035
Gargiulo, C., Gaglione, F., Guida, C., Papa, R., Zucaro, F., & Carpentieri, G. (2020). The role of the urban settlement system in the spread of Covid-19 pandemic. The Italian case. Tema. Journal of Land Use, Mobility and Environment, 189-212. https://doi.org/10.6092/1970-9870/6864
Getis, A. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3).
Google (2021). Google Mobility Report 2021-04-29 Cantabria (Spain). https://www.gstatic.com/covid19/mobility/2021-04-29_ES_Cantabria_Mobility_Report_es.pdf
Hosseini, M.R., Fouladi-Fard, R., & Aali, R. (2020). COVID-19 pandemic and sick building syndrome. Indoor and Built Environment, 29(8), 1181-1183. https://doi.org/10.1177/1420326X20935644
Kendall, M. G., & Stuart, A. (1976). The Advanced Theory of Statistics. Distribution Theory, 1. Griffin.
Li, Y., Duan, S., Yu, I. T. S., & Wong, T.W. (2004). Multi-zone modelling of probable SARS virus transmission by airflow between flats in Block E, Amoy Gardens. Indoor Air, 15, 96-111. https://doi.org/10.1111/j.1600-0668.2004.00318.x
Mansour, S., Al Kindi, A., Al-Said, Al., Al-Said, Ad., & 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. https://doi.org/10.1016/j.scs.2020.102627
Marín-Cots, P., & Palomares-Pastor, M. (2020). En un entorno de 15 minutos. Hacia la ciudad de proximidad, y su relación con el Covid-19 y la crisis climática: El caso de Málaga. Ciudad y Territorio. Estudios Territoriales, LII, 205, 685-700. https://doi.org/10.37230/CyTET.2020.205.13.3
Moran, P. (1948) The Interpretation of Statistical Maps. Journal of the Royal Statistical Society, 10, 243-251.
Mou, Y., He, Q., & Zhou, B. (2017). Detecting the spatially non-stationary relationships between housing price and its determinants in China: Guide for housing market sustainability. Sustainability, 9(10), 1826. https://doi.org/10.3390/su9101826
Nguyen, Q. C., Huang, Y., Kumar, A., Duan, H., Keralis, J. M., Dwivedi, P., Meng, H-W., Brunisholz, K. D., Jay, J., Javanmardi, M., & Tasdizen, T. (2020). Using 164 million google street view images to derive built environment predictors of COVID-19 cases. International Journal of Environmental Research and Public Health, 17(17), 6359. https://doi.org/10.3390/ijerph17176359
Perles, M.-J., Sortino, J. F., & Mérida, M. F. (2021). The neighborhood contagion focus as 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, 3145. https://doi.org/10.3390/ijerph18063145
Pinter-Wollman, N., Jelic, A., & Wells, N. M. (2018). The impact of the built environment on health behaviours and disease transmission in social systems. Philosophical Transactions R. Soc. B., 373, 20170245. http://dx.doi.org/10.1098/rstb.2017.0245
Rahman, M. H., Zafri, N. M., Ashik, F. R., & Waliullah, M. (2020). GIS-based spatial modelling to identify factors affecting COVID-19 incidence rates in Bangladesh. Medrxiv. The preprint server for health sciences. https://doi.org/10.1101/2020.08.16.20175976
Roy, A., & Kar, B. (2020). Characterizing the Spread of COVID-19 from Human Mobility Patterns and Sociodemographic Indicators. In 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC’20), November 3–6, Seattle, WA, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3423455.3430303
Salama, A. M. (2020). Coronavirus questions that will not go away: interrogating urban and socio-spatial implications of COVID-19 measures. Emerald Open Research, 2(14). https://doi.org/10.35241/emeraldopenres.13561.1
Xu, C., Luo, X., Yu, C., & Cao, S. J. (2020). The 2019-nCoV epidemic control strategies and future challenges of building healthy smart cities. Indoor and Built Environment, 29(5), 639-644. https://doi.org/10.1177/1420326X20910408
You, Y., & Pan, S. (2020). Urban vegetation slows down the spread of coronavirus disease (COVID-19) in United States. Geophysical Research Letters, 47. https://doi.org/10.1029/2020GL089286
Zúñiga, M., Pueyo, A., & Postigo, R. (2020). Herramientas espaciales para la mejora de la gestión de la información en alerta sanitaria por COVID-19. Geographicalia, 72, 141-145. https://doi.org/10.26754/ojs_geoph/geoph.2020725005