Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19

[EN] Objective The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COV...

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Detalles Bibliográficos
Autores: Garrido, J.M., Martínez Rodríguez, David, Rodríguez-Serrano, F., Pérez-Villares, J.M., Ferreiro-Marzal, A., Jiménez-Quintana, M.M., Grupo de Estudio COVID 19 Granada, Villanueva Micó, Rafael Jacinto|||0000-0002-0131-0532
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:español
OAI Identifier:oai:riunet.upv.es:10251/183133
Acceso en línea:https://riunet.upv.es/handle/10251/183133
Access Level:acceso abierto
Palabra clave:COVID-19
SARS-CoV-2
Mathematical model
Hospitalization
ICU
Pandemic
Prevalence
Epidemiological prediction
Modelo matemático
Pandemia
Predicción epidemiológica
Prevalencia
MATEMATICA APLICADA
Descripción
Sumario:[EN] Objective The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. Design Prospective study. Setting Province of Granada (Spain). Population COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. Study variables The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. Results The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. Conclusions The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

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