Advances on Uncertainty Quantification Techniques for Dynamical Systems: Theory and Modelling

[ES] La cuantificación de la incertidumbre está compuesta por una serie de métodos y técnicas computacionales cuyo objetivo principal es describir la aleatoriedad presente en problemas de diversa índole. Estos métodos son de utilidad en la modelización de procesos biológicos, físicos, naturales o sociales, ya que en ellos aparecen ciertos aspectos que no pueden ser determinados de manera exacta. Por ejemplo, la tasa de contagio de una enfermedad epidemiológica o el factor de crecimiento de un volumen tumoral dependen de factores genéticos, ambientales o conductuales. Estos no siempre pueden definirse en su totalidad y por tanto conllevan una aleatoriedad intrínseca que afecta en el desarrollo final. El objetivo principal de esta tesis es extender técnicas para cuantificar la incertidumbre en dos áreas de las matemáticas: el cálculo de ecuaciones diferenciales fraccionarias y la modelización matemática. Las derivadas de orden fraccionario permiten modelizar comportamientos que las derivadas clásicas no pueden, como por ejemplo los efectos de memoria o la viscoelasticidad en algunos materiales. En esta tesis, desde un punto de vista teórico, se extenderá el cálculo fraccionario a un ambiente de incertidumbre, concretamente en el sentido de la media cuadrática. Se presentarán problemas de valores iniciales fraccionarios aleatorios. El cálculo de la solución, la obtención de las aproximaciones de la media y varianza de la solución y la aproximación de la primera función de densidad de probabilidad de la solución son conceptos que se abordarán en los próximos capítulos. Sin embargo, no siempre es sencillo obtener la solución exacta de un problema de valores iniciales fraccionario aleatorio. Por ello en esta tesis también se dedicará un capítulo para describir un procedimiento numérico que aproxime su solución. Por otro lado, desde un punto de vista más aplicado, se desarrollan técnicas computacionales para cuantificar la incertidumbre en modelos matemáticos. Combinando estas técnicas junto con modelos matemáticos apropiados, se estudiarán problemas de dinámica biológica. En primer lugar, se determinará la cantidad de portadores de meningococo en España con un modelo de competencia de Lotka-Volterra fraccionario aleatorio. A continuación, el volumen de un tumor mamario se modelizará mediante un modelo logístico con incertidumbre. Finalmente ayudándonos de un modelo matemático que describe el nivel de glucosa en sangre de un paciente diabético, se pretende dar una recomendación de carbohidratos e insulina que se debe de ingerir para que el nivel de glucosa del paciente esté dentro de una banda de confianza saludable. Es importante subrayar que para poder realizar estos estudios se requieren datos reales, los cuales pueden estar alterados debido a los errores de medición o proceso que se han cometido para obtenerlos. Por este motivo, modelizar correctamente el problema junto con la incertidumbre en los datos es de vital importancia.[CA] La quantificació de la incertesa està composada per una sèrie de mètodes i tècniques computacionals, l'objectiu principal de les quals és descriure l'aleatorietat present en problemes de diversa índole. Aquests mètodes són d'utilitat en la modelització de processos biològics, físics, naturals o socials, ja que en ells apareixen certs aspectes que no poden ser determinats de manera exacta. Per exemple, la taxa de contagi d'una malaltia epidemiològica o el factor de creixement d'un volum tumoral depenen de factors genètics, ambientals o conductuals. Aquests no sempre poden definir-se íntegrament i per tant, comporten una aleatorietat intrínseca que afecta en el desenvolupament final. L'objectiu principal d'aquesta tesi doctoral és estendre tècniques per a quantificar la incertesa en dues àrees de les matemàtiques: el càlcul d'equacions diferencials fraccionàries i la modelització matemàtica. Les derivades d'ordre fraccionari permeten modelitzar comportaments que les derivades clàssiques no poden, com per exemple, els efectes de memòria o la viscoelasticitat en alguns materials. En aquesta tesi, des d'un punt de vista teòric, s'estendrà el càlcul fraccionari a un ambient d'incertesa, concretament en el sentit de la mitjana quadràtica. Es presentaran problemes de valors inicials fraccionaris aleatoris. El càlcul de la solució, l'obtenció de les aproximacions de la mitjana i, la variància de la solució i l'aproximació de la primera funció de densitat de probabilitat de la solució són conceptes que s'abordaran en els pròxims capítols. No obstant això, no sempre és senzill obtindre la solució exacta d'un problema de valors inicials fraccionari aleatori. Per això en aquesta tesi també es dedicarà un capítol per a descriure un procediment numèric que aproxime la seua solució. D'altra banda, des d'un punt de vista més aplicat, es desenvolupen tècniques computacionals per a quantificar la incertesa en models matemàtics. Combinant aquestes tècniques juntament amb models matemàtics apropiats, s'estudiaran problemes de dinàmica biològica. En primer lloc, es determinarà la quantitat de portadors de meningococ a Espanya amb un model de competència de Lotka-Volterra fraccionari aleatori. A continuació, el volum d'un tumor mamari es modelitzará mitjançant un model logístic amb incertesa. Finalment ajudant-nos d'un model matemàtic que descriu el nivell de glucosa en sang d'un pacient diabètic, es pretén donar una recomanació de carbohidrats i insulina que s'ha d'ingerir perquè el nivell de glucosa del pacient estiga dins d'una banda de confiança saludable. És important subratllar que per a poder realitzar aquests estudis es requereixen dades reals, els quals poden estar alterats a causa dels errors de mesurament o per la forma en que s'han obtés. Per aquest motiu, modelitzar correctament el problema juntament amb la incertesa en les dades és de vital importància.[EN] Uncertainty quantification collects different methods and computational techniques aimed at describing the randomness in real phenomena. These methods are useful in the modelling of different processes as biological, physical, natural or social, since they present some aspects that can not be determined exactly. For example, the contagious rate of a epidemiological disease or the growth factor of a tumour volume depend on genetic, environmental or behavioural factors. They may not always be fully described and therefore involve uncertainties that affects on the final result. The main objective of this PhD thesis is to extend techniques to quantify the uncertainty in two mathematical areas: fractional calculus and mathematical modelling. Fractional derivatives allow us to model some behaviours that classical derivatives cannot, such as memory effects or the viscoelasticity of some materials. In this PhD thesis, from a theoretical point of view, fractional calculus is extended into the random framework, concretely in the mean square sense. Initial value problems will be studied. The calculus of the analytic solution, approximations for the mean and for the variance and the computation of the first probability density function are concepts we deal with them thought the following chapters. Nevertheless, it is not always possible to obtain the analytic solution of an initial value problem. Therefore, in this dissertation a chapter is addressed to describe a numerical procedure to approximate the solution for an initial value problem. On the other hand, from a modelling point of view, computational techniques to quantify the uncertainty in mathematical models are developed. Merging these techniques with appropriate mathematical models, problems of biological dynamics are studied. Firstly, the carriers of meningococcus in Spain are determined using a competition Lotka-Volterra random fractional model. Then, the volume of breast tumours is modelled by a random logistic model. Finally, taking advantage of a mathematical model which describes the glucose level of a diabetic patient, a recommendation of insulin shots and carbohydrate intakes is proposed to a patient in order to maintain her/his glucose level in a healthy confidence range. An important observation is that to carry out these studies real data is required and they may include uncertainties contained in the measurements on the process to perform the corresponding study. This it is the reason why it is crucial to properly model the problem taking also into account the randomness of the data.Burgos Simón, C. (2021). Advances on Uncertainty Quantification Techniques for Dynamical Systems: Theory and Modelling [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/166442TESI

An offer and supply model with uncertaint

[EN] The stationary oer and supply models are useful in order to describe different macro-economical scenarios, for this reason they are one of the most essential tools in different subjects like Macroeconomics taught in the University degrees like Business Administration and Management and similar. The students of those degrees learn simultaneously mathematics and statistics, where they study deterministic and stochastic tools in order to apply them in that kind of models. In this work we present a simple example in order to work in a multidisciplinary way three different subjects, macroeconomics, mathematics and statistics. In particular, we will randomize the linear offer and supply model, considering that the parameters of the model are random variables instead single values. That is, because in some cases the parameters of the model have been tted using real data, and this data has an intrinsic randomness which it could begiven by two different factors, the device or the way we measure the data or the innate randomness to the consumer behaviour. Using the Random Variable Transformation Technique we can obtain the probability density function of the offer and supply functions. Moreover, we will obtain the distributions of the price and amount of equilibrium in the trade. Finally we will show the theoretical results with a numerical example.[ES] El modelo lineal estático de oferta y demanda es uno de los modelos fundamentales que se estudia en la asignatura de Microeconomía del Grado de Administración y Dirección de Empresas y en otras titulaciones afines. Los estudiantes de estas titulaciones cursan simultáneamente asignaturas de Matemáticas y de Estadística, donde se presentan herramientas determinísticas y estocásticas, respectivamente, que tienen gran aplicabilidad en los modelos que aparecen en Economía. El trabajo que se presenta proporciona un ejemplo sencillo para trabajar de forma multidisciplinar las tres asignaturas anteriormente citadas. Concretamente, se propone la aleatorización del modelo lineal de oferta y demanda, considerando que los parámetros que aparecen en dicho modelo son variables aleatorias, en lugar de constantes deterministas. La motivación de esta “randomización”se basa en el hecho de que en la práctica dichos parámetros deben ajustarse a partir de muestras que contienen la incertidumbre asociada no solo a los errores del muestreo, sino también a la complejidad inherente al comportamiento de consumidor, que influye en la oferta del mercado. Utilizando técnicas de transformación de variables aleatorias, en el trabajo se determina la función de densidad de probabilidad de la funciones de oferta y demanda. Además, dado su interés, obtenemos las distribuciones del precio y de la cantidad de equilibrio del mercado. Posteriormente, los resultados teóricos previamente establecidos son aplicados en un ejemplo numérico haciendo uso de datos obtenidos sintéticamente.Burgos Simón, C.; Cortés López, JC.; Martínez Rodríguez, D.; Navarro Quiles, A.; Villanueva Micó, RJ. (2019). Un modelo de oferta y demanda con incertidumbre. Modelling in Science Education and Learning. 12(1):111-122. https://doi.org/10.4995/msel.2019.10897SWORD111122121Martínez Torres, O. A. (2014). Análisis Económico, México. Astra ediciones.Aquino, Rita. "Teoría de la oferta y la demanda". GestioPolis. 31 enero 2008. Web. https://www.gestiopolis.com/teoria-de-la-oferta-y-la- demandaDeGroot, M.H. (1988). Probabilidad y Estadística, Ed. Addison-Wesley Iberoamericana. Madrid.Soong, T.T. (1973). Random Differential Equations in Science and Engineering. Ed. Academic Press. New York.Principle of Macroeconomics. ISBN: 978-1-946135-17-9. Ed. Libreraries Publishing, 2016

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Solving a class of random non-autonomous linear fractional differential equations by means of a generalized mean square convergent power series

The aim of this paper is to solve a class of non-autonomous linear fractional differential equations with random inputs. A mean square convergent series solution is constructed in the case that the fractional order of that Caputo derivative lies in using a random Fröbenius approach. The analysis is conducted by using the so-called mean square random calculus. The mean square convergence of the series solution is established assuming mild conditions on random inputs (diffusion coefficient and initial condition). We show that these conditions are satisfied for a variety of unbounded random variables. In addition, explicit expressions to approximate the mean, the variance and the covariance functions of the random series solution are given. Two full illustrative examples are shown

Modified SIQR model for the COVID‐19 outbreak in several countries

In this paper, we propose a modified Susceptible-Infected-Quarantine-Recovered (mSIQR) model, for the COVID-19 pandemic. We start by proving the well-posedness of the model and then compute its reproduction number and the corresponding sensitivity indices. We discuss the values of these indices for epidemiological relevant parameters, namely, the contact rate, the proportion of unknown infectious, and the recovering rate. The mSIQR model is simulated, and the outputs are fit to COVID-19 pandemic data from several countries, including France, US, UK, and Portugal. We discuss the epidemiological relevance of the results and provide insights on future patterns, subjected to health policies.The author CP was partially supported by CMUP (UID/-MAT/00144/2013), which is funded by Fundação para a Ciência e Tecnologia (FCT) (Portugal) with national (MEC) and European structural funds European Regional Development Fund (FEDER), under the partnership agreement PT2020. The revision of this paper has been overshadowed by Prof Tenreiro Machado's departure on October 2021. In sorrow, we dedicate this work to his memoryinfo:eu-repo/semantics/publishedVersio

Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities

[EN] We consider a randomized discrete logistic equation to describe the dynamics of breast tumor volume. We propose a method, that takes advantage of the principle of maximum entropy, to assign reliable distributions to model inputs (initial condition and coefficients) and sample data, respectively. Since the distributions of coefficients depend on certain parameters, we design a computational procedure to determine the above mentioned parameters using the information of the probabilistic distributions. The proposed method is successfully applied to model the breast tumor volume using real data. The approach seems to be flexible enough to be adapted to other stochastic models in future contributions.This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI), and Fondo Europeo de Desarrollo Regional (FEDER UE) Grants MTM2017-89664-P and RTI2018-095180-B-I00.Burgos-Simón, C.; Cortés, J.; Martínez-Rodríguez, D.; Villanueva Micó, RJ. (2020). Modeling breast tumor growth by a randomized logistic model: A computational approach to treat uncertainties via probability densities. European Physical Journal Plus. 135(10):1-14. https://doi.org/10.1140/epjp/s13360-020-00853-3S11413510D. Delen, G. Walker, A. Kadam, Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)H.-C. Wei, Mathematical modeling of tumor growth: the mcf-7 breast cancer cell line 16(mbe–16–06–325), 6512 (2019). https://doi.org/10.3934/mbe.2019325N. 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A model for type I diabetes in an HIV-infected patient under highly active antiretroviral therapy

Type 1 diabetes (T1D), previously known as juvenile diabetes or insulin-dependent diabetes, is an autoimmune disease characterized by the insufficient (or lack of) production of insulin by the pancreas. Insulin is crucial to maintain blood sugar at healthy levels. High blood sugar damages the body and causes a variety of symptoms, ranging from severe thirst, fatigue, to urinary infections. The cells responsible for the production of insulin are the -cells. In T1D, these are killed by an abnormal response of the immune system. Specific clones of cytotoxic T-cells invade the pancreatic islets of Langerhans, and eliminate them. T1D diabetes may develop in human immunodeficiency virus (HIV)-infected patients, though in rare situations. In this paper, we propose a cell model for the development of T1D in these patients, after immune restoration, during highly active antiretroviral therapy (HAART). The study includes the derivation of the qualitative properties of the model, and its comprehensive investigation via path-following methods, using the continuation platform COCO. In this way, the main theoretical predictions are verified in detail. Furthermore, this numerical part establishes accurate parameter thresholds to ensure an effective disease treatment in HIV-infected persons to prevent the development of T1D.The author CP was partially supported by CMUP, which is financed by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project with reference UIDB/00144/2020.info:eu-repo/semantics/publishedVersio

Constructing reliable approximations of the random fractional Hermite equation: solution, moments and density

We extend the study of the random Hermite second-order ordinary differential equation to the fractional setting. We first construct a random generalized power series that solves the equation in the mean square sense under mild hypotheses on the random inputs (coefficients and initial conditions). From this representation of the solution, which is a parametric stochastic process, reliable approximations of the mean and the variance are explicitly given. Then, we take advantage of the random variable transformation technique to go further and construct convergent approximations of the first probability density function of the solution. Finally, several numerically simulations are carried out to illustrate the broad applicability of our theoretical findings