Rodríguez Aguilar, RománRománRodríguez Aguilar2022-10-242022-10-24202197830306983869783030698393https://scripta.up.edu.mx/handle/20.500.12552/178210.1007/978-3-030-69839-3_9The recent COVID-19 pandemic has represented a great challenge for health systems around the world. That is why it is necessary to propose strategies for prioritizing care and containing the pandemic. This work proposes the use of spectral clustering to characterize high-priority areas of care based on key information on the performance of the pandemic as well as health system variables. The result shows the generation of high priority areas not only due to the deaths observed but also due to the clinical, demographic and health system variables. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.enCOVID-19PriorizationSpectral clusterUnsupervised modelsResource Types::text::conference output::conference proceedings::conference paper