Colección especial COVID-19

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https://riunet7pre.upv.es/handle/10251/147394

Esta colección especial recoge todo tipo de materales relacionados con la COVID-19 o de los coronavirus en general como aportación al mejor y más extenso conocimiento de estas enfermedades, como artículos o informes de investigación o materiales más divulgativo en las que ha participado la UPV.

 RDA Recomendaciones y pautas sobre el intercambio de datos para COVID-19,



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Browsing Colección especial COVID-19 by Sponsor "Fundación Ramón Areces"

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    Publication
    Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months
    (MDPI AG, 2021-05-17) Garrido, Jose M.; Martínez-Rodríguez, David; Rodriguez-Serrano, Fernando; Sferle, Sorina Madalina; Villanueva Micó, Rafael Jacinto; Facultad de Administración y Dirección de Empresas; Dpto. de Matemática Aplicada; Instituto Universitario de Matemática Multidisciplinar; GENERALITAT VALENCIANA; Fundación Ramón Areces; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund
    [EN] Mathematical models have been remarkable tools for knowing in advance the appropriate time to enforce population restrictions and distribute hospital resources. Here, we present a mathematical Susceptible-Exposed-Infectious-Recovered (SEIR) model to study the transmission dynamics of COVID-19 in Granada, Spain, taking into account the uncertainty of the phenomenon. In the model, the patients moving throughout the hospital's departments (intra-hospitalary circuit) are considered in order to help to optimize the use of a hospital's resources in the future. Two main seasons, September-April (autumn-winter) and May-August (summer), where the hospital pressure is significantly different, have been included. The model is calibrated and validated with data obtained from the hospitals in Granada. Possible future scenarios have been simulated. The model is able to capture the history of the pandemic in Granada. It provides predictions about the intra-hospitalary COVID-19 circuit over time and shows that the number of infected is expected to decline continuously from May without an increase next autumn-winter if population measures continue to be satisfied. The model strongly suggests that the number of infected cases will reduce rapidly with aggressive vaccination policies. The proposed study is being used in Granada to design public health policies and perform wise re-distribution of hospital resources in advance.
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    Publication
    Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19
    (Elsevier, 2022-05) Garrido, J.M.; Martínez Rodríguez, David; Rodríguez-Serrano, F.; Pérez-Villares, J.M.; Ferreiro-Marzal, A.; Jiménez-Quintana, M.M.; Villanueva Micó, Rafael Jacinto; Grupo de Estudio COVID 19 Granada; Facultad de Administración y Dirección de Empresas; Dpto. de Matemática Aplicada; Instituto Universitario de Matemática Multidisciplinar; GENERALITAT VALENCIANA; Fundación Ramón Areces; AGENCIA ESTATAL DE INVESTIGACION
    [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.