Colección especial COVID-19
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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.
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- PublicationA case study of 2019-nCOV cases in Argentina with the real data based on daily cases from March 03, 2020 to March 29, 2021 using classical and fractional derivatives(2021-07-20) Kumar, Pushpendra; Erturk, Vedat Suat; Murillo Arcila, Marina; Banerjee, Ramashis; Manickam, A.; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund[EN] In this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana-Baleanu type fractional-order model and simulate it by using predictor-corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper.
- PublicationA new fractional mathematical modelling of COVID-19 with the availability of vaccine(Elsevier, 2021-05) Kumar, Pushpendra; Erturk, Vedat Suat; Murillo Arcila, Marina; AGENCIA ESTATAL DE INVESTIGACION[EN] The most dangerous disease of this decade novel coronavirus or COVID-19 is yet not over. The whole world is facing this threat and trying to stand together to defeat this pandemic. Many countries have defeated this virus by their strong control strategies and many are still trying to do so. To date, some countries have prepared a vaccine against this virus but not in an enough amount. In this research article, we proposed a new SEIRS dynamical model by including the vaccine rate. First we formulate the model with integer order and after that we generalize it in Atangana-Baleanu derivative sense. The high motivation to apply Atangana-Baleanu fractional derivative on our model is to explore the dynamics of the model more clearly. We provide the analysis of the existence of solution for the given fractional SEIRS model. We use the famous Predictor-Corrector algorithm to derive the solution of the model. Also, the analysis for the stability of the given algorithm is established. We simulate number of graphs to see the role of vaccine on the dynamics of the population. For practical simulations, we use the parameter values which are based on real data of Spain. The main motivation or aim of this research study is to justify the role of vaccine in this tough time of COVID-19. A clear role of vaccine at this crucial time can be realized by this study.
- 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.
- PublicationThe effects on European importers' food safety controls in the time of COVID-19(Elsevier, 2021-07) Martí Selva, María Luisa; Puertas Medina, Rosa María; García Alvarez-Coque, José María; Facultad de Administración y Dirección de Empresas; Dpto. de Economía y Ciencias Sociales; Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural; Grupo de Investigación de Economía Internacional y Desarrollo; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund[EN] COVID-19 has highlighted the fragility of the global economic system. In just a few months, the consequences of the pandemic have left their mark on the affected countries at all levels and without exception. This article analyses the profile of food safety notifications reported by European countries in the first five months of 2020. The aim was to detect possible changes in food safety regulations imposed by control authorities that could aggravate the economic impacts of the pandemic. While COVID-19 does not appear to be a foodborne disease, some outbreaks have been linked to imported food, which might have affected the food control behaviour of importing countries. In this study, contingency tables and clustering were used to assess differences between years and notification characteristics and to detect homogeneous groups to help identify how the reported notifications might have changed. In the period considered in this study, the volume of notifications on most imported foodstuffs decreased considerably. This decrease was a direct consequence of the fall in international trade, which might have increased countries' reliance on domestic sources. The COVID-19 crisis has not caused a substantial change in the profile of European countries¿ in terms of the characteristics of reported notifications (product category and risk decision). However, the worst affected countries have replaced border rejections with alerts, which may indicate greater reliance on intra-EU markets.
- PublicationThe Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis(MDPI AG, 2021-05) González, Luis-Millán; Devis-Devis, Jose; Pellicer-Chenoll, Maite; Pans, Miquel; Pardo-Ibáñez, Alberto; García-Massó, Xavier; Peset Mancebo, María Fernanda; Garzón-Farinós, Fernanda; Pérez-Samaniego, Víctor; Dpto. de Comunicación Audiovisual, Documentación e Historia del Arte; Facultad de Administración y Dirección de Empresas; Instituto Universitario de Matemática Pura y Aplicada; AGENCIA ESTATAL DE INVESTIGACION[EN] The spread of the SARS-CoV-2 virus has transformed many aspects of people's daily life, including sports. Social networks have been flooded on these issues. The present study aims to analyze the tweets produced relating to sports and COVID-19. From the end of January to the beginning of May 2020, over 4,000,000 tweets on this subject were downloaded through the Twitter search API. Once the duplicates, replicas, and retweets were removed, 119,253 original tweets were analyzed. A quantitative-qualitative content analysis was used to study the selected tweets. Posts dynamics regarding sport and exercise evolved according to the COVID-19 pandemic and subsequent lockdown, shifting from considering sport as a healthy bastion to an activity exposed to disease like any other. Most media professional sporting events received great attention on Twitter, while grassroots and women's sport were relegated to a residual role. The analysis of the 30 topics identified focused on the social, sporting, economic and health impact of the pandemic on the sport. Sporting cancellations, leisure time and socialization disruptions, club bankruptcies, sports training and athletes' uncertain career development were the main concerns. Although general health measures appeared in the tweets analyzed, those addressed to sports practice were relatively scarce. Finally, this study shows the importance of Twitter as a means of conveying social attitudes towards sports and COVID-19 and its potential to generate alternative responses in future stages of the pandemic.