Minería de datos y patrones socio-espaciales de la COVID-19: claves de geoprevención para hacer frente a la pandemia

RESUMEN: 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.ABSTRCT: A geographic perspective is essential in tackling COVID-19. This research study is framed in the collaboration project set up by the University of Cantabria, the Valdecilla Hospital Research Institute (IDIVAL) and the Regional Government of Cantabria. The case study is the Santander functional urban area (FUA), which is considered from a multi-scale perspective. The main source is the daily records of micro-data on COVID-19 cases and the methodology is based on ESRI geo-technologies, and more specifically on a tool called SITAR (a Spanish acronym which stands for Fast-Action Territorial Information System). The main goal is to analyse and contribute to knowledge of the spatial patterns of COVID-19 at neighbourhood level from a space-time perspective. To that end the research is based on data mining methods (3D bins and emerging hot-spots) and exploratory geo-statistical analysis (Global Moran?s Index, Nearest Neighbourhood and Ordinary Least Square analyses, among others). The study identifies space-time patterns that show significant hot-spots and demonstrates a high presence of the virus at building level in neighbourhoods where residential and economic uses are mixed. Knowing the spatial behaviour of the virus is strategically important for proposing geo-prevention keys, reducing spread and balancing trade-offs between potential health gains and economic burdens resulting from interventions to deal with the pandemic.This research was funded by the Government of Cantabria (Spain) under grant number UC: 10.3834.64001 “Assistance in adapting Cantabria to the plan for the transition to a new normal in times of Covid-19: socioeconomic contributions” from the University of Cantabria-IDIVAL Valdecilla-Government of Cantabria

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans

Several studies on spatial patterns of COVID-19 show huge differences depending on the country or region under study, although there is some agreement that socioeconomic factors a ect these phenomena. The aim of this paper is to increase the knowledge of the socio-spatial behavior of coronavirus and implementing a geospatial methodology and digital system called SITAR (Fast Action Territorial Information System, by its Spanish acronym). We analyze as a study case a region of Spain called Cantabria, geocoding a daily series of microdata coronavirus records provided by the health authorities (Government of Cantabria-Spain) with the permission of Medicines Ethics Committee from Cantabria (CEIm, June 2020). Geocoding allows us to provide a new point layer based on the microdata table that includes cases with a positive result in a COVID-19 test. Regarding general methodology, our research is based on Geographical Information Technologies using Environmental Systems Research Institute (ESRI) Technologies. This tool is a global reference for spatial COVID-19 research, probably due to the world-renowned COVID-19 dashboard implemented by the Johns Hopkins University team. In our analysis, we found that the spatial distribution of COVID-19 in urban locations presents a not random distribution with clustered patterns and density matters in the spread of the COVID-19 pandemic. As a result, large metropolitan areas or districts with a higher number of persons tightly linked together through economic, social, and commuting relationships are the most vulnerable to pandemic outbreaks, particularly in our case study. Furthermore, public health and geoprevention plans should avoid the idea of economic or territorial stigmatizations. We hold the idea that SITAR in particular and Geographic Information Technologies in general contribute to strategic spatial information and relevant results with a necessary multi-scalar perspective to control the pandemic.This research was funded by Government of Cantabria (Spain) grant number UC: 10.3834.64001 called “Asistencia en la adecuación de Cantabria al plan para la transición haca una nueva normalidad en tiempos del Covid-19: aportaciones socioeconómicas” University of Cantabria—IDIVAL Valdecilla—Government of Cantabria (Spain). And the APC was funded by IDIVAL support program

Differencing the Risk of Reiterative Spatial Incidence of COVID-19 Using Space-Time 3D Bins of Geocoded Daily Cases

ABSTRACT: The space-time behaviour of COVID-19 needs to be analysed frommicrodata to understand the spread of the virus. Hence, 3D space-time bins and analysis of associated emerging hotspots are useful methods for revealing the areas most at risk from the pandemic. To implement these methods, we have developed the SITAR Fast Action Territorial Information System using ESRI technologies. We first modelled emerging hotspots of COVID-19 geocoded cases for the region of Cantabria (Spain), then tested the predictive potential of the method with the accumulated cases for two months ahead. The results reveal the difference in risk associated with areas with COVID-19 cases. The study not only distinguishes whether a bin is statistically significant, but also identifies temporal trends: a reiterative pattern is detected in 58.31% of statistically significant bins (most with oscillating behaviour over the period). In the testing method phase, with positive cases for two months ahead, we found that only 7.37% of cases were located outside the initial 3D bins. Furthermore, 83.02% of new cases were in statistically significant previous emerging hotspots. To our knowledge, this is the first study to show the usefulness of the 3D bins and GIS emerging hotspots model of COVID-19 microdata in revealing strategic patterns of the pandemic for geoprevention plans

The role of functional urban areas in the spread of COVID-19 Omicron (Northern Spain)

This study focuses on the space-time patterns of the COVID-19 Omicron wave at a regional scale, using municipal data. We analyze the Basque Country and Cantabria, two adjacent regions in the north of Spain, which between them numbered 491,816 confirmed cases in their 358 municipalities from 15th November 2021 to 31st March 2022. The study seeks to determine the role of functional urban areas (FUAs) in the spread of the Omicron variant of the virus, using ESRI Technology (ArcGIS Pro) and applying intelligence location methods such as 3D-bins and emerging hot spots. Those methods help identify trends and types of problem area, such as hot spots, at municipal level. The results demonstrate that FUAs do not contain an over-concentration of COVID-19 cases, as their location coefficient is under 1.0 in relation to population. Nevertheless, FUAs do have an important role as drivers of spread in the upward curve of the Omicron wave. Significant hot spot patterns are found in 85.0% of FUA area, where 98.9% of FUA cases occur. The distribution of cases shows a spatially stationary linear correlation linked to demographically progressive areas (densely populated, young profile, and with more children per woman) which are well connected by highways and railroads. Based on this research, the proposed GIS methodology can be adapted to other case studies. Considering geo-prevention and WHO Health in All Policies approaches, the research findings reveal spatial patterns that can help policymakers in tackling the pandemic in future waves as society learns to live with the virus.This research was funded by the research project INNVAL20/03 (IDIVAL) entitled “Test de estrés o resistencia en el Sistema Cántabro de Salud, desarrollo de tecnologías innovadoras digitales para modelizar escenarios de mayor utilización sanitaria y soluciones de impacto socioeconómico y humano frente a la COVID-19.

A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

ABSTRACT: The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at the regional scale and the socio-economic context of problem areas over the 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification and analysis of COVID-19 microdata registered by health authorities of the Government of Cantabria, Spain from the beginning of the pandemic register (29th February 2020) to 2nd December 2021. The spatial behaviour of the virus was studied using ArcGIS Pro and a 1x1 km vector grid as the homogeneous reference layer. The GIS analysis of 45,392 geocoded cases revealed a clear process of spatial contraction of the virus after the spread in 2020 with 432 km2 of problem areas reduced to 126.72 km2 in 2021. The socio-economic framework showed complex relationships between COVID-19 cases and the explanatory variables related to household characteristics, socio-economic conditions and demographic structure. Local bivariate analysis showed fuzzier results in persistent hotspots in urban and peri-urban areas. Questions about ?where, when and how? contribute to learning from experience as we must draw inspiration from, and explore connections to, those confronting the issues related to the current pandemic

Cooperación internacional al desarrollo: cartografía colaborativa en los sectores de Rukara y Huye (Rwanda)

El proyecto de Cartografía Colaborativa en los sectores de Rukara y Huye (Rwanda), como acción geográfica de cooperación al desarrollo, se ha centrado en la implementación de una cartografía generada desde cero por estudiantes locales de enseñanza secundaria dentro de un proyecto de cartografía participativa como es OpenStreetMap, mediante la colaboración de cuatro socios: Colegio de Geógrafos de España, Universidad de Alicante, Nacional University of Rwanda y la ONGD Nueva Fraternidad de Torrevieja (Alicante)

Diagnóstico Territorial del Litoral de Cantabria. Volumen I: Informe.

Estudios de base para la redacción del Plan de Ordenación del Litoral (POL) de Cantabria.Este proyecto de investigación aplicada se ha realizado gracias al Convenio de Colaboración entre el Gobierno de Cantabria y la Universidad de Cantabria titulado “Diagnóstico Territorial del Litoral de Cantabria”

Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain

After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geostatistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases.This study is part of IDIVAL’s PRIMVAL-2021 research project (Code PRIMVAL21/01), entitled “Escenarios post-vacunación de la COVID-19: papel de la atención primaria ante la aparición de nuevos casos. Seguimiento y costes”. Spatial analysis methods are implemented using the Universidad de Cantabria’s ESRI geo-technological software (ArcGIS Pro) License

Spatiotemporal multiscale diagnosis model to proactively respond to the multi-country monkeypox virus outbreak in 2022

This research analyzes the spatiotemporal trend of 23,121 monkeypox virus cases in the multi-country outbreak that affected 82 countries from January 2022 to July 2022. The spatiotemporal trends analysis is developed using open data and GIS to model 3D bins and emerging hot spots globally (data by country) and nationally (data by region) for hardest hit countries, like the USA and Spain.The implemented methodology distinguishes between problem areas -as significant hot spots- and countries with no pattern.Results show consecutive hot spot patterns in Western Europe and high location quotients in North America. Factually, the countries with consecutive patterns record 16,494 cases, that is, 71.34% of the cases, where 7.63% of the world population live. At the national level, in the analysis of the USA and Spain, the results reveal regional differences with significative hot spots in California and on the East Coast of the USA and the Mediterranean coast of Spain. The proposed methodology facilitates the monitoring of the spatiotemporal evolution of monkeypox cases and is scalable and replicable using non-arbitrary and statistical parameters. The findings indicate problematic zones in real-time, enabling policymakers to develop focused interventions and proactive strategies to mitigate the future risk of monkeypox.This work was supported by the Valdecilla Biomedical Research Institute (IDIVAL) [Research projects INNVAL 20/03 and PRIMVAL21/01] and by the Universidad de Cantabria [License of geo-technological software Esri, ArcGIS Pro]

Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

ABSTRACT: This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging threedimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policymakers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management