Colección especial COVID-19
Permanent URI for this collection
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,Browse
Browsing Colección especial COVID-19 by Sponsor "GENERALITAT VALENCIANA"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- PublicationCOVIDSensing: Social Sensing strategy for the management of the COVID-19 crisis(MDPI AG, 2021-12) Sepúlveda, Alicia; Periñán Pascual, José Carlos; Muñoz, Andrés; Martínez-España, Raquel; Hernández Orallo, Enrique; Cecilia Canales, José María; Escuela Técnica Superior de Ingeniería de Telecomunicación; Dpto. de Informática de Sistemas y Computadores; Dpto. de Lingüística Aplicada; Escuela Politécnica Superior de Gandia; Escuela Técnica Superior de Ingeniería Informática; Grupo de Análisis de las Lenguas de Especialidad (GALE); Grupo de Redes de Computadores; GENERALITAT VALENCIANA; AGENCIA ESTATAL DE INVESTIGACION; Agencia Estatal de Investigación; European Regional Development Fund[EN] The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders constitute a large critical mass dispersed everywhere and with an immediate capacity for information transfer. The main goal of this article is to present a novel methodological tool based on social sensing, called COVIDSensing. In particular, this application serves to provide actionable information in real time for the management of the socio-economic and health crisis caused by COVID-19. This tool dynamically identifies socio-economic problems of general interest through the analysis of people¿s opinions on social networks. Moreover, it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures together with the social perceptions towards the disease. This article presents the case study of Spain to illustrate the tool.
- PublicationImproving prediction of COVID-19 evolution by fusing epidemiological and mobility data(Nature Publishing Group, 2021-07-26) García-Cremades, Santi; Morales-García, Juan; Hernández-Sanjaime, Rocío; Martínez-España, Raquel; Bueno-Crespo, Andrés; Hernández Orallo, Enrique; López-Espín, José J.; Cecilia Canales, José María; Escuela Técnica Superior de Ingeniería de Telecomunicación; Dpto. de Informática de Sistemas y Computadores; Escuela Técnica Superior de Ingeniería Informática; Grupo de Redes de Computadores; European Social Fund; GENERALITAT VALENCIANA; AGENCIA ESTATAL DE INVESTIGACION; Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia; Conselleria d'Educació, Investigació, Cultura i Esport de la Generalitat Valenciana[EN] We are witnessing the dramatic consequences of the COVID¿19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID¿19 pandemic to create a decision support system for policy¿makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14¿day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14¿day CI in scenarios with and without trend changes, reaching 0.93 R2, 4.16 RMSE and 1.08 MAE.
- PublicationModeling 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.
- PublicationModelo 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.