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In 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 epidemiologic, Ministerio de Ciencia e Innovación, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

CRUE-CSIC (Acuerdos Transformativos 2023), In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (Parallel Weighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model., Ministerio de Ciencia e Innovación (MICINN), Junta de Andalucía, Universidad Complutense de Madrid, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

In this paper, we develop a stochastic model for topology optimization. We find robust structures that minimize the compliance for a given main load having a stochastic behavior. We propose a model that takes into account the expected value of the compliance and its variance. We show that, similarly to the case of truss structures, these values can be computed with an equivalent deterministic approach and the stochastic model can be transformed into a nonlinear programming problem, reducing the complexity of this kind of problems. First, we obtain an explicit expression (at the continuous level) of the expected compliance and its variance, then we consider a numerical discretization (by using a finite element method) of this expression and finally we use an optimization algorithm. This approach allows to solve design problems which include point, surface or volume loads with dependent or independent perturbations. We check the capacity of our formulation to generate structures that are robust to main loads and their perturbations by considering several 2D and 3D numerical examples. To this end, we analyze the behavior of our model by studying the impact on the optimized solutions of the expected-compliance and variance weight coefficients, the laws used to describe the random loads, the variance of the perturbations and the dependence/independence of the perturbations. Then, the results are compared with similar ones found in the literature for a different modeling approach., Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, inpress

We present a method to factorize a second-order boundary value problem in a cylindrical domain in a system of uncoupled first-order initial value problems, together with a nonlinear Riccati-type equation for functional operators. This uncoupling is obtained by a space invariant embedding technique along the axis of the cylinder. This method can be viewed as an infinite-dimensional generalization of the block Gauss LU factorization., Ministerio de Ciencia y Tecnología of Spain, Ramón y Cajal, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

CRUE-CSIC (Acuerdos Transformativos 2021), 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 u, Ministerio de Ciencia, Innovación y Universidades (España), Universidad Complutense de Madrid, Fondo Europeo de Desarrollo Regional, Junta de Andalucía, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

Since 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., Depto. de Economía Financiera y Actuarial y Estadística, Depto. de Análisis Matemático y Matemática Aplicada, Instituto de Matemática Interdisciplinar (IMI), Fac. de Ciencias Económicas y Empresariales, Fac. de Ciencias Matemáticas, FALSE, pub

We study the static equilibria of a simplified Leslie–Ericksen model for a unidirectional uniaxial nematic flow in a prototype microfluidic channel, as a function of the pressure gradient G and inverse anchoring strength, B. We numerically find multiple static equilibria for admissible pairs (G,B) and classify them according to their winding numbers and stability. The case G=0 is analytically tractable and we numerically study how the solution landscape is transformed as G increases. We study the one-dimensional dynamical model, the sensitivity of the dynamic solutions to initial conditions and the rate of change of G and B. We provide a physically interesting example of how the time delay between the applications of G and B can determine the selection of the final steady state., Ministerio de Economía y Competitividad (MINECO), Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

The low-weight newborns and especially the premature infants have difficulty in maintaining their temperature in the range considered to be normal. Several studies revealed the importance of thermal environment and moisture to increase the survival rate of newborns. This work models the process of heat exchange and energy balance in premature newborns during the first hours of life in a closed incubator. In addition, a control problem was proposed and solved in order to maintain thermal stability of premature newborns to increase their rate of survival and weight. For this purpose, we propose an algorithm to control the temperature inside the incubator. It takes into account the measurements of the body temperature of a premature newborn which are recorded continuously. We show that using this model the temperature of a premature newborn inside the incubator can be kept in a thermal stability range., Comunidad de Madrid, Autonomous University of Puebla-Mexico, Spanish Ministry of Education, Culture and Sport, Ministry of Economy and Competitiveness, Fundación Caja Madrid, European Social Fund, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

In this paper, we develop a stochastic model for topology optimization. We find robust structures that minimize the compliance for a given main load having a stochastic behavior. We propose a model that takes into account the expected value of the compliance and its variance. We show that, similarly to the case of truss structures, these values can be computed with an equivalent deterministic approach and the stochastic model can be transformed into a nonlinear programming problem, reducing the complexity of this kind of problems. First, we obtain an explicit expression (at the continuous level) of the expected compliance and its variance, then we consider a numerical discretization (by using a finite element method) of this expression and finally we use an optimization algorithm. This approach allows to solve design problems which include point, surface or volume loads with dependent or independent perturbations. We check the capacity of our formulation to generate structures that are robust to main loads and their perturbations by considering several 2D and 3D numerical examples. To this end, we analyze the behavior of our model by studying the impact on the optimized solutions of the expected-compliance and variance weight coefficients, the laws used to describe the random loads, the variance of the perturbations and the dependence/independence of the perturbations. Then, the results are compared with similar ones found in the literature for a different modeling approach., Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, inpress

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., Comunidad de Madrid, Ministerio de Ciencia e Innovación (MICINN), Research group MOMAT, Banco Santander, Universidad Complutense de Madrid (UCM), European Social Fund, Science Foundation Ireland Mathematics Initiative, Mathematics Applications Consortium for Science and Industry (MACSI), Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

We present a method to factorize a second-order boundary value problem in a cylindrical domain in a system of uncoupled first-order initial value problems, together with a nonlinear Riccati-type equation for functional operators. This uncoupling is obtained by a space invariant embedding technique along the axis of the cylinder. This method can be viewed as an infinite-dimensional generalization of the block Gauss LU factorization., Ministerio de Ciencia y Tecnología of Spain, Ramón y Cajal, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, Instituto de Matemática Interdisciplinar (IMI), TRUE, pub

We study the approximate controllability property for y(t) - Delta phi(y) = u chi(omega), on Omega x (0, T), where Omega is a bounded open set of R-N and omega subset of Omega. First, we show some negative results for the case phi(s) = \s\(m-1)s, 0 < m < 1, by means of an obstruction phenomenon. In a second part, we obtain a positive answer on the space H-1-gamma(Omega), for any gamma > 0, for a class of functions phi essentially linear at infinity, by using a higher order vanishing viscosity argument., Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub