Person:
Vela Pérez, María

First Name

María

Last Name

Vela Pérez

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Económicas y Empresariales

Department

Economía Financiera, Actuarial y Estadística

Area

Estadística e Investigación Operativa

Identifiers

Full item page

Search Results

Now showing 1 - 9 of 9

Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China
(Elsevier, 2020-04-30) Ivorra, Benjamin; Ferrández, M.R.; Vela Pérez, María; Ramos, Ángel Manuel
In this paper we develop a mathematical model for the spread of the coronavirus disease 2019 (COVID-19). We use a compartmental model (but not a SIR, SEIR or other general purpose model) and take into account the known special characteristics of this disease, as the existence of infectious undetected cases. We study the particular case of China (including Chinese Mainland, Macao, Hong-Kong and Taiwan, as done by the World Health Organization in its reports about COVID-19), the country spreading the disease, and use its reported data to identify the modelparameters, which can be of interest for estimating the spread of COVID-19 in other countries. The model is also able to estimate the needs of beds in hospitals for intensive care units. Finally, we also study the behavior of the outputs returned by our model when considering incomplete data (by truncating them at some dates before and after the peak of daily reported cases). By comparing those results with real observation we can estimate the error produced by the model when identifying the parameters at early stages of the epidemic.
Modeling the impact of SARS-CoV-2 variants and vaccines on the spread of COVID-19
(Elsevier, 2021-06-24) Ramos del Olmo, Ángel Manuel; Vela Pérez, María; Ferrández, M.R.; Kubik, Alicja Barbara; Ivorra, Benjamin
The continuous mutation of SARS-CoV-2 opens the possibility of the appearance of new variants of the virus with important differences in its spreading characteristics, mortality rates, etc. On 14 December 2020, the United Kingdom reported a potentially more contagious coronavirus variant, present in that country, which is referred to as VOC 202012/01. On 18 December 2020, the South African government also announced the emergence of a new variant in a scenario similar to that of the UK, which is referred to as variant 501.V2. Another important milestone regarding this pandemic was the beginning, in December 2020, of vaccination campaigns in several countries. There are several vaccines, with different characteristics, developed by various laboratories and research centers. A natural question arises: what could be the impact of these variants and vaccines on the spread of COVID-19? Many models have been proposed to simulate the spread of COVID-19 but, to the best of our knowledge, none of them incorporates the effects of potential SARS-CoV-2 variants together with the vaccines in the spread of COVID-19. We develop here a -SVEIHQRD mathematical model able to simulate the possible impact of this type of variants and of the vaccines, together with the main mechanisms influencing the disease spread. The model may be of interest for policy makers, as a tool to evaluate different possible future scenarios. We apply the model to the particular case of Italy (as an example of study case), showing different outcomes. We observe that the vaccines may reduce the infections, but they might not be enough for avoiding a new wave, with the current expected vaccination rates in that country, if the control measures are relaxed. Furthermore, a more contagious variant could increase significantly the cases, becoming the most common way of infection. We show how, even with the pandemic cases slowing down (with an effective reproduction number less than 1) and the disease seeming to be under control, the effective reproduction number of just the new variant may be greater than 1 and, eventually, the number of infections would increase towards a new disease wave. Therefore, a rigorous follow-up of the evolution of the number of infections with any potentially more dangerous new variant is of paramount importance at any stage of the pandemic.
Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods
(MDPI, 2020-06-18) García Cuesta, Esteban; Gómez Vergel, Daniel; Gracia Expósito, Luis; López López, Jose M.; Vela Pérez, María
Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.
Orientación y sistemas ecológicos mediante señales locales
(Universidad Complutense de Madrid, Servicio de Publicaciones, 2011-10-18) Vela Pérez, María; López Velázquez, Juan José; Fontelos López, Marco Antonio
Estrategia de gamificación aplicada a métodos estadísticos en el proceso de aprendizaje en enseñanzas turísticas
(2020-01-23) Barreal Pernas, Jesús; Jannes, Gil; Albert García, Carlos; Vela Pérez, María; Gutiérrez Salinero, María Ángeles; López Fernández, Angel Luis; Arroyo Barrigüete, José Luis; Montiel Ben Allal, Nora; Agustín Herrera, Alma; Suarez-Varela Marti, Olga; Krak, Darya; González Gutiérrez, Nebiur; Ferrando Aguirre, Daniel; López Domingo, Belén; Lluna Taverner, Francisco José; Cisneros Martínez, José David
Las redes sociales como modo de establecer un vínculo estable con los egresados de las titulaciones de la Facultad de Ciencias Económicas y Empresariales
(2022) Rivero Rodríguez, Carlos; Alonso Guinea, Fernando; Díaz-Bustamante Ventisca, Mónica; Fernández Sánchez, Rafael; Fossas Olalla, Marta; Martínez Alós, Paloma Isabel; Martínez Rodríguez, María Elena; Sánchez Quirós, Isabel; Vela Pérez, María
El objetivo es desarrollar, a través de las redes sociales, herramientas que establezcan un vínculo estable con los egresados de las titulaciones de la Facultad, facilitando el seguimiento de su inserción laboral y desarrollo profesional
Big data en educación II: metodologías adaptativas en el proceso de enseñanza-aprendizaje desde el diagnóstico del estudiante
(2019-01-22) Hernández Estrada, Adolfo; García Pérez, Enrique; Fernández-Cid Enríquez, Matilde; Vela Pérez, María; Peñaloza Figueroa, Juan Luis; Martínez Rodríguez, María Elena; Arteaga Martínez, Blanca; Macías Sánchez, Jesús; Martín Apaolaza, Nirian; Fernández-Crehuet Santos, José María; Pérez Martín, María; Mateos-Aparicio Morales, Gregoria; Fernández Molina, María Elia; Dorado Sánchez, Juan; Ruozzi López, Alberto; Martíns Pinto, Ana Rita; Martínez de La Fuente, Jorge Iván; Andrés García, Ángel de; Carrasco Pradas, M. Desamparados; Álvarez Sáez, Manuel; Ferrer Caja, José María; Aparicio Sánchez, María del Socorro; Barreal Pernas, Jesús; Jannes, Gil
Diseño y puesta en marcha en la Facultad de Ciencias Económicas y Empresariales de un plan de formación para el profesorado para la adaptación a la enseñanza semipresencial en el curso 2020-2021
(2021-06) Rivero Rodríguez, Carlos; Alonso Guinea, Fernando; Camacho Miñano, María del Mar; Fernández Sánchez, Rafael; Fossas Olalla, Marta; Martínez Alós, Paloma Isabel; Martínez Rodríguez, María Elena; Sánchez Quirós, Isabel; Vela Pérez, María
Big data en educación: tipologías de los estudiantes a partir del estudio de las interacciones dentro del triángulo pedagógico
(2017-09-29) Hernández Estrada, Adolfo; Martínez Rodríguez, María Elena; Casado de Lucas, David; Peñaloza Figueroa, Juán Luis; Pérez Martín, María; Arteaga Martínez, Blanca; Martín Apaolaza, Nirian; Macías Sánchez, Jesús; Fernández Molina, María Elia; Ruozzi López, Alberto; Martins Pinto, Ana Rita; Martínez de la Fuente, Jorge Ivan; Fernández-Crehuet Santos, José María; Vela Pérez, María; Dorado Sánchez, Juán Francisco