Spatial disparities in incidence of COVID-19 in relation to economic and socio-demographic factors in the Autonomous Community of Madrid, Spain
This article models the relationship between the incidence of COVID-19 and several socioeconomic factors during the second period of epidemic (22 June 2020 to 06 December 2020) in the Autonomous Community of Madrid, Spain. Data collected from Basic Health Zones (BHZs) is adjusted using the random fo...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:300358 |
| Acceso en línea: | https://ddd.uab.cat/record/300358 https://dx.doi.org/urn:doi:10.5565/rev/dag.904 |
| Access Level: | acceso abierto |
| Palabra clave: | COVID-19 Economic factors Random forest method Autonomous Community of Madrid Socio-demographic factors Factors econòmics Factors sociodemogràfics Bosc aleatori Comunitat Autònoma de Madrid Factores económicos Factores sociodemográficos Bosque aleatorio Comunidad Autónoma de Madrid Acteurs économiques Facteurs sociodémographiques Forêts aléatoires Communauté Autonome de Madrid |
| Sumario: | This article models the relationship between the incidence of COVID-19 and several socioeconomic factors during the second period of epidemic (22 June 2020 to 06 December 2020) in the Autonomous Community of Madrid, Spain. Data collected from Basic Health Zones (BHZs) is adjusted using the random forest method, which proves very appropriate for capturing non-linear relationships and obtaining accurate and robust predictions. The results show that the impact of the examined socio-economic variables on rates of incidence of COVID-19 was not uniform, and that levels of mean income by neighborhood exerted stronger influence than population density, proportion of the Spanish population, mean age of the population or average household size. A complex spatial pattern emerges from the combination of impacts, reflecting the relative weights of the different factors in terms of intensity of the pandemic. This information may be considered strategic for the effective future management of health resources. |
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