Person: Ramos Del Olmo, Ángel Manuel
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First Name
Ángel Manuel
Last Name
Ramos Del Olmo
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Análisis Matemático Matemática Aplicada
Area
Matemática Aplicada
Identifiers
10 results
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Item A simple but complex enough -SIR type model to be used with COVID-19 real data. Application to the case of Italy(Physica D: nonlinear phenomena, 2021) Fernández, M. R.; Kubik, Alicja Barbara; Ivorra, Benjamín Pierre Paul; Vela Pérez, María; Ramos Del Olmo, Ángel ManuelSince the start of the COVID-19 pandemic in China many models have appeared in the literature, trying to simulate its dynamics. Focusing on modeling the biological and sociological mechanisms which influence the disease spread, the basic reference example is the SIR model. However, it is too simple to be able to model those mechanisms (including the three main types of control measures: social distancing, contact tracing and health system measures) to fit real data and to simulate possible future scenarios. A question, then, arises: how much and how do we need to complexify a SIR model? We develop a -SEIHQRD model, which may be the simplest one satisfying the mentioned requirements for arbitrary territories and can be simplified in particular cases. We show its very good performance in the Italian case and study different future scenarios.Item Analysis and simplification of a mathematical model for high-pressure food processes(Applied Mathematics and Computation, 2014) Ramos Del Olmo, Ángel Manuel; Smith, Nadia A. S; Mitchell, S. L.Nowadays, consumers look for minimally processed, additive-free food products that maintain their organoleptic properties. This has led to the development of new technologies for food processing. One emerging technology is high hydrostatic pressure, as it proves to be very effective in prolonging the shelf life of foods without losing its properties. Recent research has involved modelling and simulating the effect of combining thermal and high pressure processes (see Denys et al. (2000), Infante et al. (2009), Knoerzer et al. (2007), Otero et al. (2007)). The focus is mainly on the inactivation of certain enzymes and microorganisms that are harmful to food. Various mathematical models that study the behaviour of these enzymes and microorganisms during a high pressure process have been proposed (see Infante et al. (2009), Knoerzer et al. (2007)). Such models need the temperature and pressure profiles of the whole process as an input. In this paper we present two dimensional models, with different types of boundary conditions, to calculate the temperature profile for solid type foods. We give an exact solution and propose several simplifications, in both two and one dimensions. The temperature profile of these simplified two and one dimensional models is calculated both numerically and analytically, and the solutions are compared. Our results show a very good agreement for all the approximations proposed, and so we can conclude that the simplifications and dimensional reduction are reasonable for certain parameter values, which are specified in this work.Item Epidemiological impacts of attenuated African swine fever virus circulating in wild boar populations(Research in veternary science, 2023) Martínez Avilés, Marta; Bosch López, Jaime Alfonso; Ivorra, Benjamín Pierre Paul; Ramos Del Olmo, Ángel Manuel; Ito, Satoshi; Barasona García-Arévalo, José Ángel; Sánchez-Vicario, José ManuelAfrican swine fever virus (ASFV) genotype II has been present in wild boar in the European Union since 2014. Control measures have reduced the incidence of the ASF, but highly virulent as well as attenuated ASFV strains continue to circulate. We present the intraherd epidemiological parameters of low and highly virulent ASFV in wild boar from experimental data, and for the first time, evaluate the impact of attenuated strain circulation through unique deterministic compartmental model simulations under various potential scenarios and hypotheses. Using an estimated PCR infectious threshold of TPCR = 36.4, we obtained several transmission parameters, like an Rx (experimental intraherd R0) value of 4.5. We also introduce two novel epidemiological parameters: infectious power and resistance power, which indicate the ability of animals to transmit the infection and the reduction in infectiousness after successive exposures to varying virulence strains, respectively. The presence of ASFV attenuated strains results in 4–17% of animals either remaining in a carrier state or becoming susceptible again when exposed to highly virulent ASFV for more than two years. The timing between exposures to viruses of different virulence also influences the percentage of animals that die or remain susceptible. The findings of this study can be utilized in epidemiological modelling and provide insight into important risk situations that should be considered for surveillance and future potential ASF vaccination strategies in wild boar.- Project number: PIMCD337/23-24
Item Proyecto de control y seguimiento de la calidad de la producción científica del IMI(2024) Ramos Del Olmo, Ángel Manuel; Vitoriano Villanueva, Begoña; Petkova Petkova, Tzveta; Salinas Trejo, Álvaro; Herrero Hervás, Federico; Caamaño Cristóbal, José Ignacio; Porqueras Arabolaza, Fernando; Ramos Del Olmo, Ángel Manuel; Herrero Hervás, FedericoDesde el Instituto de Matemática Interdisciplinar (IMI), se propone la implantación de un nuevo método de gestión de la información relativa a las publicaciones científicas de los miembros del Instituto. Este procedimiento, basado en la creación de una base de datos que recoja los principales aspectos bibliométricos de las publicaciones, permitirá al Instituto llevar a cabo un seguimiento interno de la calidad de los trabajos científicos de los miembros del Instituto. Además, se busca ampliar la difusión de las publicaciones a través de los medios de los que dispone el Instituto: su web y el boletín semanal de noticias, llamado Boletín del IMI. Para poder llevar a cabo esta labor, se requiere un equipo y soporte informático adecuado que permita su constante actualización, lo cual supondría la principal parte del presupuesto solicitado. La parte restante se destinaría a maximizar la difusión de las publicaciones y actividades organizadas por el Instituto mediante la adquisición de material fungible y adquisición de DOIs. El equipo está conformado por dos PDI, los Profesores Ángel Manuel Ramos del Olmo, del dpto. de Análisis Matemático y Matemática Aplicada, y Begoña Vitoriano Villanueva, del dpto. de Estadística e Investigación Operativa; dos PAS, Tzveta Petkova Petkova y Fernando Porqueras Arabolaza; y dos estudiantes, Federico Herrero Hervás y Álvaro Salinas Trejo, siendo así de carácter interdisciplinar e interdepartamental, contando con miembros de cada categoría. Item Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union(Applied Mathematical Modelling, 2023) Capistrán Ocampo, Marcos Aurelio; Infante Del Río, Juan Antonio; Ramos Del Olmo, Ángel Manuel; Rey Cabezas, José MaríaIn this work, we manage to disentangle the role of virus infectiousness and awarenessbased human behavior in the COVID-19 pandemic. Using Bayesian inference, we quantify the uncertainty of a state-space model whose propagator is based on an unusual SEIRtype model since it incorporates the effective population fraction as a parameter. Within the Markov Chain Monte Carlo (MCMC) algorithm, Unscented Kalman Filter (UKF) may be used to evaluate the likelihood approximately. UKF is a suitable strategy in many cases, but it is not well-suited to deal with non-negativity restrictions on the state variables. To overcome this difficulty, we modify the UKF, conveniently truncating Gaussian distributions, which allows us to deal with such restrictions. We use official infection notification records to analyze the first 22 weeks of infection spread in each of the 27 countries of the European Union (EU). It is known that such records are the primary source of information to assess the early evolution of the pandemic and, at the same time, usually suffer underreporting and backlogs. Our model explicitly accounts for uncertainty in the dynamic model parameters, the dynamic model adequacy, and the infection observation process. We argue that this modeling paradigm allows us to disentangle the role of the contact rate, the effective population fraction, and the infection observation probability across time and space with an imperfect first principles model. Our findings agree with phylogenetic evidence showing little variability in the contact rate, or virus infectiousness, across EU countries during the early phase of the pandemic, highlighting the advantage of incorporating the effective population fraction into pandemic modeling for heterogeneity in both human behavior and reporting. Finally, to evaluate the consistency of our data assimilation method, we performed a forecast that adequately fits the actual data. Statement of significance: Data-driven and model-based epidemiological studies aimed at learning the number of people infected early during a pandemic should explicitly consider the behavior-induced effective population effect. Indeed, the non-isolated, or effective, fraction of the population during the early phase of the pandemic is time-varying, and first-principles modeling with quantified uncertainty is imperative for an adequate analysis across time and space. We argue that, although good inference results may be obtained using the classical SEIR type model, the model posed in this work has allowed us to disentangle the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union from official infection notification records.Item Modelling the COVID-19 pandemic: variants and vaccines(2022) Kubik Gladys, Alicia Barbara; Ferrández, Miriam R.; Vela Pérez, María; Ivorra, Benjamín Pierre Paul; Ramos Del Olmo, Ángel ManuelOn December 2019, a new virus emerged and started to spread through the Chinese city of Wuhan, the SARS-CoV-2. On 30 January 2020, the WHO declared the COVID-19as a public health emergency of international concern. On 11 March 2020, it is declared the first pandemic caused by a coronavirus. Since then, lifestyle has been notably conditioned to this fact, and researchers have been working quickly and hard to improve the understanding of this unknown disease and shed some light on this situation. This last year, new SARS-CoV-2 variants have emerged and most of the European population is fully vaccinated against COVID-19. Here, we present a θ-SIR model that has been tested with real data during this pandemic. It is an improvement of previous models (see [1, 2]) -now we incorporate new compartments to consider vaccination and divide each infectious compartment depending on the amount of different SARS-CoV-2 variants, to finally apply it to the territory of Italy (see [3]). Compartmental models are very used models to treat epidemics mathematically. One of the main advantages of these models is the fact that its parameters are directly related to real biological processes, and hence they may give intuition about the functioning of some unknown processes (for example, the real magnitude of the pandemic studying the evolution of the asymptomatic cases, or estimations of the number of beds needed). The main result of the article presented here (i.e., [3]) is the definition of an effective reproduction number Rt for each variant – this led us to foresee the high probability of the variant Alpha generating a third wave in Italy, which finally happened.Item Modeling the impact of SARS-CoV-2 variants and vaccines on the spread of COVID-19(Communications in Nonlinear Science and Numerical Simulation, 2021) Ramos Del Olmo, Ángel Manuel; Vela Pérez, María; Ivorra, Benjamín Pierre Paul; Ferrández, M.R.; Kubik, Alicja BarbaraThe 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.- Project number: 214
Item Escenarios multimedia en formación de futuros profesores universitarios de matemáticas (ESCEMMAT-Univ)(2019) Gómez Chacón, Inés María; Baró González, Elías; Barbero, Marta; Capel Cuevas, Ángela; Caravantes Tortajada, Jorge; Contreras Tejada, Patricia; Díaz-Cano Ocaña, Antonio; Folgueira López, Marta; Gómez Castro, David; González Prieto, José Ángel; González Ortega, Jorge; Ivorra, Benjamín Pierre Paul; Juan Llamas, María Del Carmen; Melle Hernández, Alejandro; Pe Pereira, María; Prieto Yerro, María Ángeles; Sánchez Benito, María Mercedes; Ramos Del Olmo, Ángel ManuelPreparar al profesorado novel de matemáticas para una docencia universitaria de calidad, mediante el desarrollo de competencias y conocimiento estratégico para aprender a enseñar Matemáticas. Se desarrollan ejemplificaciones para ser implementadas. Item A Note on Probability and Conditional Probability SVM models(2024) Carrasco, Miguel; López, Julio; Ivorra, Benjamín Pierre Paul; Ramos Del Olmo, Ángel ManuelSupport Vector Machines (SVMs) are powerful tools in machine learning, widely used for classification and regression tasks. Over time, various extensions, such as Probability Estimation SVM (PSVM) and Conditional Probability SVM (CPSVM), have been proposed to enhance SVM performance across different conditions and datasets. This article offers a comprehensive review and analysis of SVMs, with a particular focus on the PSVM and CPSVM models. We delve into the intricacies of these models, addressing computational nuances and presenting corrected formulations where necessary. Our empirical evaluation, conducted on diverse benchmark datasets, implements both linear and nonlinear versions of these SVMs. Performance is benchmarked using the Balanced Accuracy metric. The results highlight the comparative strengths of these models in handling varied datasets and their potential advantages over traditional SVM formulations. To rigorously assess and compare the performance of these SVM variants, we employ statistical tests, including the Friedman test and post hoc analysis.Item Embedded Feature Selection for Robust Probability Learning Machines(2024) Carrasco, Miguel; Ivorra, Benjamín Pierre Paul; López, Julio; Ramos Del Olmo, Ángel ManuelFeature selection is essential for building effective machine learning models in binary classification. Eliminating unnecessary features can reduce the risk of overfitting and improve classification performance. Moreover, the data we handle always has a stochastic component, making it important to have robust models that are insensitive to data perturbations. Although there are numerous methods and tools for feature selection, relatively few works deal with embedded feature selection performed with robust classification models. In this work, we introduce robust classifiers with integrated feature selection capabilities, utilizing probability machines based on different penalization techniques such as the L1-norm or the elastic-net, combined with a novel Direct Feature Elimination process. Numerical experiments on standard databases demonstrate the effectiveness and robustness of the proposed models in classification tasks with a reduced number of features, using original indicators.The study also discusses the trade-offs in combining different penalties to select the most relevant features while minimizing empirical risk.