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.

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.

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 â€Ëœwhere, 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.

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

The space–time behaviour of COVID-19 needs to be analysed from microdata 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.