2063 research outputs found
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Modeling spillover dynamics: understanding emerging pathogens of public health concern
No full textThe emergence of infectious diseases with pandemic potential is a major public health threat worldwide. The World Health Organization reports that about 60% of emerging infectious diseases are zoonoses, originating from spillover events. Although the mechanisms behind spillover events remain unclear, mathematical modeling offers a way to understand the intricate interactions among pathogens, wildlife, humans, and their shared environment. Aiming at gaining insights into the dynamics of spillover events and the outcome of an eventual disease outbreak in a population, we propose a continuous time stochastic modeling framework. This framework links the dynamics of animal reservoirs and human hosts to simulate cross-species disease transmission. We conduct a thorough analysis of the model followed by numerical experiments that explore various spillover scenarios. The results suggest that although most epidemic outbreaks caused by novel zoonotic pathogens do not persist in the human population, the rising number of spillover events can avoid long-lasting extinction and lead to unexpected large outbreaks. Hence, global efforts to reduce the impacts of emerging diseases should not only address post-emergence outbreak control but also need to prevent pandemics before they are established.MA acknowledges the financial support by the Ministerio de Ciencia e Innovación (MICINN) of the Spanish Government through the Ramón y Cajal Grant RYC2021-031380-I
Dynamics of vector-borne diseases through the lens of systems incorporating fractional-order derivatives
No full textThis paper explores the dynamics of vector-borne diseases transmitted by mosquitoes through the lens of systems incorporating fractional-order derivatives. Specifically, we extend the classical SISUV and SIRUV models by introducing fractional-order systems with the Caputo sense derivative. This augmentation introduces memory effects into the modelling process. Our study delves into the local and global stability analyses of both models, providing valuable insights into the dynamics of these diseases. To validate our theoretical results, we conduct numerical simulations confirming the robustness and applicability of the proposed models.This article is based upon work from COST Action CA16227 Investigation & Mathemati-
cal Analysis of Avant-garde Disease Control via Mosquito Nano-Tech-Repellents, supported by
COST (European Cooperation in Science and Technology). Dorota Mozyrska was supported by
Bialystok University of Technology, Grant Number WZ/WI-IIT/2/2023 and funded by the resources for research by the Ministry of Education and Scienc
From Monte Carlo to neural networks approximations of boundary value problems
No full textIn this paper we study probabilistic and neural network approximations for solutions to Poisson
equation subject to H¨ older data in general bounded domains of Rd. We aim at two fundamental goals.
The first, and the most important, we show that the solution to Poisson equation can be numer-
ically approximated in the sup-norm by Monte Carlo methods, and that this can be done highly
efficiently if we use a modified version of the walk on spheres algorithm as an acceleration method.
This provides estimates which are efficient with respect to the prescribed approximation error and
with polynomial complexity in the dimension and the reciprocal of the error. A crucial feature is
that the overall number of samples does not not depend on the point at which the approximation is
performed.
As a second goal, we show that the obtained Monte Carlo solver renders in a constructive way
ReLU deep neural network (DNN) solutions to Poisson problem, whose sizes depend at most poly-
nomialy in the dimension dand in the desired error. In fact we show that the random DNN provides
with high probability a small approximation error and low polynomial complexity in the dimension
On the collective effect of a large system of heavy particles immersed in a Newtonian fluid
No full textWe consider the motion of a large number of heavy particles in a Newtonian fluid occupying
a bounded spatial domain. When we say “heavy”, we mean a particle with a mass density that
approaches infinity at an appropriate rate as its radius vanishes. We show that the collective
effect of heavy particles on the fluid motion is similar to the Brinkman perturbation of the
Navier–Stokes system identified in the homogenization process
Fundamental interactions in self-organized critical dynamics on higher-order networks
Get PDFIn functionally complex systems, higher-order connectivity is often revealed in the underlying
geometry of networked units. Furthermore, such systems often show signatures of self-organized
criticality, a specific type of non-equilibrium collective behaviour associated with an attractor of
internal dynamics with long-range correlations and scale invariance, which ensures the robust
functioning of complex systems, such as the brain. Here, we highlight the intertwining of features
of higher-order geometry and self-organized critical dynamics as a plausible mechanism for the
emergence of new properties on a larger scale, representing the central paradigm of the physical
notion of complexity. Considering the time scale of the structural evolution with the known
separation of the time scale in self-organized criticality, i.e., internal dynamics and external
driving, we distinguish three classes of geometries that can shape the self-organized dynamics on
them differently. We provide an overview of current trends in the study of collective dynamics
phenomena, such as the synchronization of phase oscillators and discrete spin dynamics with
higher-order couplings embedded in the faces of simplicial complexes. For a representative
example of self-organized critical behaviour induced by higher-order structures, we present a
more detailed analysis of the dynamics of field-driven spin reversal on the hysteresis loops in
simplicial complexes composed of triangles. These numerical results suggest that two fundamental
interactions representing the edge-embedded and triangle-embedded couplings must be taken into
account in theoretical models to describe the influence of higher-order geometry on critical dynamics
Transforming Combinatorial Optimization Problems in Fourier Space: Consequences and Uses
Get PDFWe analyze three permutation-based combinatorial optimization problems in Fourier space, namely, the quadratic assignment problem, the linear ordering problem (LOP), and the symmetric and nonsymmetric traveling salesperson problem (STSP). In previous studies, one can find a number of theorems with necessary conditions that the Fourier coefficients of the aforementioned problems must satisfy. In this manuscript, we prove the sufficiency of these conditions, which implies that they constitute the exact characterization of the problems in Fourier space. In addition, the Fourier coefficients of the LOP and the symmetric and non-STSP are completely characterized by showing certain proportionality patterns that they must follow. Taking the characterization in Fourier space of the problems as a basis, we study classes of equivalent instances of the LOP and the symmetric and non-STSP, considering that two instances are equivalent if they have the same objective function. Furthermore, we give canonical representations for each problem in such a way that the input matrices have the minimum number of nonzero parameters
Analysis of local density during football stadium access: Integrating pedestrian flow simulations and empirical data
Get PDFThis study analyzes numerically the access of football fans to a typical football stadium
through pedestrian flow simulations. With this aim, we introduce a novel framework to
address the difficulty of simulating pedestrian dynamics in highly complex geometries with
multiple accesses. The framework consists of a combination of the Social Force Model (SFM)
and Computational Fluid Dynamics tools to calculate multiple desired velocity maps. The
introduction of this framework allows the predefinition of the desired velocity fields for the
complex geometries in both the interior and surrounding area of the stadium. We validate the
results of the proposed framework using actual entry-rate data from the turnstiles of three gates
for 15 matches provided by the football club. Remarkably, the validation process allows us to
describe the temporal evolution of the spatial distribution of pedestrians during their arrival
with a single set of parameters valid for all the analyzed matches. Finally, we assess the potential
fatality risk based on the local pedestrian density varying the total number of attendees and
their arrival rate. The outcome evidences permanent clogs emerge at the entrance in the most
extreme cases with high attendance or low standard deviation of the arrival rate, indicating the
possibility of fatality occurrence.BCAM Severo Ochoa excellence accreditation CEX2021-001142-S/MICINAEI/10.13039/50110001103
Unsupervised learning approaches for disease progression modeling
Get PDFThis thesis presents methodologies for unsupervised learning from discrete sequences that define a patient's medical history. Specifically, these methods enable the modelling of the evolution of treatment trajectories associated with one or several diseases. We developed models based on various sequence classification techniques to capture the subtypes of treatments for a disease, the temporal irregularities between medical events, and the joint evolution of treatments in a patient's medical history. Furthermore, we introduce efficient methods for learning these models. We used a database provided by Osakidetza for the evaluation of the proposed methodologies, where each patient is represented by a sequence of medical services over time, with only 19% of these medical events having an associated diagnosis. We include practical applications focused on patients diagnosed with breast cancer, thereby highlighting the relevance and impact of the models in real-world situations. In summary, this thesis proposes interpretable methodologies to understand the dynamics of diseases, effectively addressing the particular challenges that arise in electronic health records.Funding in direct support of this work has been provided by the Basque Government
through the BERC 2022-2025 program and BMTF project, and by the Ministry of
Science, Innovation and Universities: BCAM Severo Ochoa accreditation CEX2021-
001142-S / MICIN / AEI/ 10.13039/501100011033. Jose A. Lozano is also supported by
the Basque Government under grant IT1504-22 and Ministry of Science and Innovation
under grant PID2022-137442NB-I00. Onintze Zaballa also holds a predoctoral grant
(EJ-GV 2019) from the Basque Government
Statistical Modelling for Recurrent Events in Sports Injury Research with Applications to Football InjuryData.
Get PDFSports injuries stand as undesirable side effects of athletic participation, carrying serious consequencesfor athletes' health, their professional careers, and overall team performance. With the growing availability of data, there has been an increasing reliance on statistical models to monitor athletes' healthand mitigate injury risks.In this dissertation, our focus is on the statistical analysis of sports injury data, with an emphasis on the time-varying and recurrent nature of injury occurrences. We develop and assess suitable statistical modelling approaches to address specific research questions that arise in sports injury prevention research. We pursue three primary objectives: (a) identifying biomechanical risk factors using variableselection methods and shared frailty Cox models, (b) developing a flexible recurrent time-to-event approach to model the effects of training load on subsequent injuries, and (c) creating dedicated statistical tools through the open-source R software. These objectives are driven by interdisciplinary research, conducted in close collaboration with the Medical Services of Athletic Club, and are motivated by real-world applications. Namely, the work is based on three distinct data sets: the functional screening tests data, the external training load data, and the web-scraped football injury data. The statistical advancements developed contribute to ongoing efforts in sports injury prevention, providinginsights, methodologies, and accessible software implementations for sports medicine practitioners.This research was supported by the Spanish Ministry of Science and Innovation (MICINN) through the Severo Ochoa SEV-2017-0718 PRE2018-
084007 funding and the BCAM Severo Ochoa accreditation CEX2021-001142-
S/MICIN/AEI/10.13039/501100011033; by the Basque Government through the BERC
2018-2021 and BERC 2022-2025 programs, and the PRE_2021_2_0029 funding; and by the
AEI/FEDER, UE through the “S3M1P4R” PID2020-115882RB-I00 project
