Potential of hazard mapping as a tool for facing COVID-19 transmission: the geo-COVID cartographic platform
Barra lateral del artículo
Contenido principal del artículo
Resumen
Potencialidad de la cartografía de peligrosidad como instrumento de lucha frente a la trasmisión de la COVID-19: plataforma cartográfica geo-COVID
El artículo recoge los fundamentos epidemiológicos, la metodología y las utilidades de la plataforma cartográfica Geo-Covid para la lucha frente a la trasmisión de la Covid-19 a nivel intraurbano. Tras un análisis de las principales carencias en el ámbito de la cartografía de riesgo a nivel vecinal, y de los fallos que inducen a un diagnóstico erróneo del patrón de trasmisión en la ciudad, se propone como complemento y alternativa una cartografía de peligrosidad de máximo detalle espacial y temporal, que se basa en el Mapa de Focos de contagio vecinal activos, gradado según distintos indicadores de peligrosidad. El catálogo cartográfico de peligrosidad de Geo-Covid se complementa con el Mapa de áreas de máximo tránsito de potenciales positivos asintomáticos, así como el de Puntos de máximo riesgo de contagio. La metodología cartográfica propuesta se aplica a distintos momentos de afección de la pandemia a la ciudad de Málaga (2020 y 2021). Se concluye que la plataforma cartográfica propuesta en el artículo (Geo-Covid) permite un análisis realista y riguroso de la distribución espacial natural de la epidemia en tiempo real. El foco de contagio vecinal se propone como unidad básica para el diagnóstico epidemiológico y la acción contra el contagio.
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Bibliografía
Adekunle, I. A., Onanuga, A. T., Akinola, O. O., & Ogunbanjo, O. W. (2020). Modelling spatial variations of coronavirus disease (COVID-19) in Africa. Science of The Total Environment, 729, 138998. https://doi.org/10.1016/j.scitotenv.2020.138998
Arsenault, J., Michel, P., Berke, O., Ravel, A., & Gosselin, P. (2013). How to choose geographical units in ecological studies: Proposal and application to campylobacteriosis. Spatial and Spatio-Temporal Epidemiology, 7, 11-24. https://doi.org/10.1016/j.sste.2013.04.004
Ayala-Carcedo, F. J., & Olcina Cantos, J. (Eds.). (2002). Riesgos naturales. Editorial Ariel.
Bamweyana, I., Okello, D.A., Ssengendo, R., Mazimwe, A., Ojirot, P., Mubiru, F., Ndungo, L., Kiyingi, C.N., Ndyabakira, A., Bamweyana, S., & Zabali, F. (2020). Socio-Economic Vulnerability to COVID-19: The Spatial Case of Greater Kampala Metropolitan Area (GKMA). Journal of Geographic Information System, 12(04), 302. https://doi.org/10.4236/jgis.2020.124019
Buzai, G.D. (2020). De Wuhan a Luján. Evolución espacial del COVID-19. Posición, 3(2683–8915), 1-21. http://ri.unlu.edu.ar/xmlui/handle/rediunlu/683
Borjas, G.J. (2020). Demographic determinants of testing incidence and COVID-19 infections in New York City neighborhood (No. w26952). National Bureau of Economic Research. https://doi.org/10.3386/w26952
Chadi, M. A., & Mousannif, H. (2020). Making Sense of the Current Covid 19 Situation and Suggesting a tailored Release Strategy through Modeling And Simulation Case Study: Casablanca, Morocco. ArXiv. https://arxiv.org/abs/2005.03477
Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021). Mobility network models of COVID-19 explain inequities and inform reopening. Nature, 589, 82-87. https://doi.org/10.1038/s41586-020-2923-3
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. ISPRS International Journal of Geo-Information, 10(4), 261. https://doi.org/10.3390/ijgi10040261
DeCapprio, D., Gartner, J., McCall, C.J., Burgess, T., Kothari, S., & Sayed, S. (2020). Building a COVID-19 vulnerability index. MedRxiv, 1-12. https://doi.org/10.1101/2020.03.16.20036723
Desai, D. (2020). Urban densities and the Covid-19 pandemic: Upending the sustainability myth of global megacities. ORF Occasional Paper, 244, 1-38. https://www.orfonline.org/wp-content/uploads/2020/05/ORF_OccasionalPaper_244_PandemicUrbanDensities.pdf
Desjardins, M. R., Hohl, A., & Delmelle, E. M. (2020). Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters. Applied Geography, 118, 102202. https://doi.org/10.1016/j.apgeog.2020.102202
Díaz-Olalla, J. M., Blasco-Novalbos, G., & Valero-Oteo, I. (2021). Incidencia de COVID-19 en distritos de Madrid y su relación con indicadores socioeconómicos y demográficos. Revista Española de Salud Pública, 95(1), e1-e14. https://dialnet.unirioja.es/servlet/articulo?codigo=8067725
Elías-Cuartas, D., Arango-Londoño, D., Guzmán-Escarria, G., Muñoz, E., Caicedo, D., Ortega-Lenis, D., … Méndez, F. (2020). Análisis espacio-temporal del SARS-coV-2 en Cali, Colombia. Revista de Salud Pública, 22(2), 1-6. https://doi.org/10.15446/rsap.v22n2.86431
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
García, C.R., Iftimi, A., Briz-Redón, Á., Zanin, M., Otero, M., Ballester, M., de Andrés, J., Landoni, G., de las Marinas, D., Catalá Bauset, J.C., Mandigorra, J., Conca, J., Correcher, J., Ferrer, C., & Lozano, M. (2021). Trends in Incidence and Transmission Patterns of COVID-19 in Valencia, Spain. JAMA Network Open, 4(6), e2113818-e2113818. https://doi.org/10.1001/jamanetworkopen.2021.13818
Garcia-Morata, M., Gonzalez-Rubio, J., Segura, T., & Najera, A. (2022). Spatial analysis of COVID-19 hospitalised cases in an entire city: The risk of studying only lattice data. Science of The Total Environment, 806, 150521. https://doi.org/10.1016/j.scitotenv.2021.150521
Gibson, L., & Rush, D. (2020). Novel Coronavirus in Cape Town Informal Settlements: Feasibility of Using Informal Dwelling Outlines to Identify High Risk Areas for COVID-19 Transmission From A Social Distancing Perspective. JMIR Public Health and Surveillance, 6(2), e18844. https://doi.org/10.2196/18844
Gross, B., Zheng, Z., Liu, S., Chen, X., Sela, A., Li, J., … Havlin, S. (2020). Spatio-temporal propagation of COVID-19 pandemics. EPL (Europhysics Letters), 131(5), 58003. https://doi.org/10.1209/0295-5075/131/58003
Hooper, M. (2020, April 13). Pandemics and the future of urban density: Michael Hooper on hygiene, public perception and the “urban penalty”. Harvard University Graduate School of Design News. https://www.gsd.harvard.edu/2020/04/have-we-embraced-urban-density-to-our-own-peril-michael-hooper-on-hygiene-public-perception-and-the-urban-penalty-in-a-global-pandemic/
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
Jacquez, G.M. (2000). Spatial analysis in epidemiology: Nascent science or a failure of GIS? Journal of Geographical Systems, 2(1), 91-97. https://doi.org/10.1007/s101090050035
Jordan, R.E., Adab, P., & Cheng, K.K. (2020). Covid-19: risk factors for severe disease and death. BMJ, 368, m1198. https://doi.org/10.1136/bmj.m1198
Kamel Boulos, M. N., & Geraghty, E. M. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbr. International Journal of Health Geographics, 19(1), 8. https://doi.org/10.1186/s12942-020-00202-8
Khatib, E. J., Perles Roselló, M. J., Miranda-Páez, J., Giralt, V., & Barco, R. (2021). Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring. Sensors, 21(10), 3424. https://doi.org/10.3390/s21103424
Lai, P.-C., So, F.-M., & Chan, K.-W. (2009). Spatial Epidemiological Approaches in Disease Mapping and Analysis. CRC Press
Lakhani, A. (2020). Which Melbourne Metropolitan Areas Are Vulnerable to COVID-19 Based on Age, Disability, and Access to Health Services? Using Spatial Analysis to Identify Service Gaps and Inform Delivery. Journal of Pain and Symptom Management, 60(1), e41-e44. https://doi.org/10.1016/j.jpainsymman.2020.03.041
Lall, S., & Wahba, S. (2020, July 18). La Construcción de Ciudades Inclusivas y Sostenibles en el Período de Recuperación de la Pandemia no es un Mito urbano. Grupo Banco Mundial. https://www.bancomundial.org/es/news/immersive-story/2020/06/18/no-urban-myth-building-inclusive-and-sustainable-cities-in-the-pandemic-recovery
Lavell, A. (2000). Sobre la gestión del riesgo: apuntes hacia una definición. Biblioteca Virtual en Salud de Desastres-OPS, 4, 1-22. https://pesquisa.bvsalud.org/portal/resource/pt/des-15036
Lawson, A. B., Banerjee, S., Haining, R. P., & Ugarte, M. D. (2016). Handbook of Spatial Epidemiology. Chapman and Hall/CRC. https://doi.org/10.1201/b19470
Llanes Michel, J., Jatib Khatib, E., Perles-Rosello, M. J., Sortino, J., Mérida, M., Miranda-Paez, J., García Almenzar, D., Miralles, J., & Barco-Moreno, R. (2021). Estudio de la correlación confluencia-contagios del Covid-19. RIUMA. https://riuma.uma.es/xmlui/handle/10630/22950
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, (205), 685-700. https://doi.org/10.37230/CyTET.2020.205.13.3
Miramontes Carballada, A., & Balsa Barreiro, J. (Preprint). Geospatial analysis and mapping strategies for fine-grained 1and detailed COVID-19 data with Geographic Information 2Systems. Research Squeare. https://assets.researchsquare.com/files/rs-273514/v1_stamped.pdf
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. https://doi.org/10.1016/j.scitotenv.2020.138884
Moore, D.A., & Carpenter, T.E. (1999). Spatial Analytical Methods and Geographic Information Systems: Use in Health Research and Epidemiology. Epidemiologic Reviews, 21(2), 143-161. https://doi.org/10.1093/oxfordjournals.epirev.a017993
Niu, X., Yue, Y., Zhou, X., & Zhang, X. (2020). How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China. ISPRS International Journal of Geo-Information, 9(11), 615. https://doi.org/10.3390/ijgi9110615
O’Reilly, K.M., Auzenbergs, M., Jafari, Y., Liu, Y., Flasche, S., & Lowe, R. (2020). Effective transmission across the globe: the role of climate in COVID-19 mitigation strategies. The Lancet Planetary Health, 4(5), e172. https://doi.org/10.1016/S2542-5196(20)30106-6
Olaya, V. (2016). Sistemas de Información Geográfica. CreateSpace Independent Publishing Platform. Retrieved from http://volaya.github.io/libro-sig/
Oliver, N., Barber, J. X., Roomp, K., & Roomp, K. (2020). Assessing the Impact of the COVID-19 Pandemic in Spain: Large-Scale, Online, Self-Reported Population Survey. Journal of medical Internet research, 22(9), e21319. https://doi.org/10.2196/21319
Openshaw, S., & Taylor, P. J. (1981). The Modifiable Areal Unit Problem. In N. Wrigley & R. Bennett (Eds.), Quantitative Geography: A British View (pp. 60-69). Routledge.
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), 1-24. https://doi.org/10.3390/ijerph18063145
Perles Roselló, M.J., Sortino Barrionuevo, J.F., Cantarero Prados, F.J., Castro Noblejas, H., de la Fuente Roselló, A.L., Orellana Macías, J.M., Reyes Corredera, S., Miranda Páez, J., & Mérida Rodríguez, M. (2020). Propuesta metodológica para la elaboración de una cartografía de riesgo de COVID19 en entornos urbanos (Research report). RIUMA. https://tinyurl.com/y3f49xnz
Pfeiffer, D.U., Robinson, T.P., Stevenson, M., Stevens, K.B., & Rogers, D.J. (2008). Spatial Analysis in Epidemiology. Oxford University Press.
Redondo Bravo, L., Suárez Rodríguez, B., Fernández, B., Soria, S., Díaz, O., José, M., & Moros, S. (2018). Epidemia por virus Zika. Respuesta desde la salud pública en España. Revista Española de Salud Pública, 92, 1-16. https://scielo.isciii.es/pdf/resp/v92/1135-5727-resp-92-e201810079.pdf
Rosenkrantz, L., Schuurman, N., Bell, N., & Amram, O. (2021). The need for GIScience in mapping COVID-19. Health and Place, 67, 102389. https://doi.org/10.1016/j.healthplace.2020.102389
Sajadi, M.M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles-Wilhelm, F., & Amoroso, A. (2020). Temperature, Humidity and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19. SSRN, 3550308. https://doi.org/10.2139/ssrn.3550308
Shaw, N. T., & Mcguire, S. K. (2017). Understanding the use of geographical information systems (GISs) in health informatics research: a review. BMJ Health & Care Informatics, 24(2), 228-233. http://dx.doi.org/10.14236/jhi.v24i2.940
Shaw, R., Kim, Y., & Hua, J. (2020). Governance, technology and citizen behavior in pandemic: Lessons from COVID-19 in East Asia. Progress in Disaster Science, 6, 100090. https://doi.org/10.1016/j.pdisas.2020.100090
Suárez, M., Valdés González, C., Pérez, C., Enrique, L., Guzmán, S., Ruiz Rivera, N., Alcántara-Ayala, I., López Cervantes, M., Rosales Tapia, A.R., Lee Alardin, W., Benítez Pérez, H., Juárez Gutiérrez, M.C., Bringas López, O.A., Oropeza Orozco, O., Peralta Higuera, A., & Garnica-Peña, R.J. (2020). Índice de vulnerabilidad ante COVID-19 en México. Investigaciones Geográficas, (104). https://doi.org/10.14350/rig.60140
UNISDR, United Nations International Strategy for Disaster Reduction (2009). Terminology on disaster risk reduction. Switzerland.
Whittle, R.S., & Diaz-Artiles, A. (2020). An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC medicine, 18(1), 1-17. https://doi.org/10.1186/s12916-020-01731-6
Xie, Z., Qin, Y., Li, Y., Shen, W., Zheng, Z., & Liu, S. (2020). Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors. Science of The Total Environment, 744, 140929. https://doi.org/10.1016/j.scitotenv.2020.140929
Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., Cao, Z., Wang, J., Yuan, W., Zhu, Y., Song, C., Chen, J., Xu, J., Li, F., Ma, T., Jiang, L., Yan, F., Yi, J., Hu, Y., Liao, Y., &… Xiao, H. (2020). COVID-19: Challenges to GIS with Big Data. Geography and Sustainability, 1(1), 77–87. https://doi.org/10.1016/j.geosus.2020.03.005
Zúñiga Antón, M., Pueyo Campos, A., & Postigo Vidal, 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