2063 research outputs found
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The impact of non-pharmaceutical interventions on COVID-19 transmission and its effect on life expectancy in two European regions
No full textBackground
In response to the rapid global transmission of COVID-19, governments worldwide enacted lockdowns and other non-pharmaceutical interventions (NPI) to control the disease. In this study, we aim to quantify the influence of NPIs on the transmission of COVID-19 within selected European regions, specifically Spain (including the Basque Country) and Italy (including Tuscany), during the period of February to December 2020, which predates the initiation of COVID-19 vaccinations. We investigate potential correlations and associations between the implementation of NPIs, changes in COVID-19 transmission rates, and alterations in life expectancy across different age and sex categories from the year 2019 to 2020.
Methods
We use a Susceptible-Hospitalized-Asymptomatic/Mild-Recovered-Deceased (SHARD) ordinary differential equations model to analyze COVID-19 dynamics in the studied regions. The model calibration process was performed with empirical data on hospitalization and death to estimate the weekly transmission and death rates. To quantify reductions in life expectancy, we used established survival analysis techniques.
Results
The SHARD model effectively captures multiple waves of COVID-19, accurately representing peaks and aligning with the instantaneous reproduction number. Our analysis reveals a 66-78% reduction in transmission rates during the initial set of NPIs in March 2020, followed by a 34-55% reduction during the subsequent NPIs in October 2020. Additionally, the elderly and individuals with comorbidities experienced the most pronounced reductions in life expectancy.
Conclusions
Our model calibration approach provides a valuable tool for evaluating the effectiveness of interventions across multiple waves of an epidemic. By applying this method to COVID-19 dynamics, we have demonstrated the capacity to quantify the impact of non-pharmaceutical interventions (NPIs) on transmission rates. These findings offer practical insights into the effectiveness of NPIs in mitigating COVID-19 spread and contribute to the broader understanding of epidemic control strategies.Ministerio de Ciencia e Innovación (MICINN) of the Spanish Government through the Ramón y Cajal grant RYC2021-031380-I
EITB Marathon 2021 call, reference BIO21/COV/00
A methodology for expert knowledge imbrication in mooring system design using Bayesian based optimization
No full textMooring system design optimisation is a complex problem requiring a specific technical expertise. Because of the large number of parameters influencing the design and their related uncertainty, efficient design methodologies and simplified cost models are unavoidable. This study proposes a methodology for the imbrication of expert knowledge on the design optimisation of mooring systems via Bayesian Optimisation (BO). A Gaussian Process Regression has been used as a surrogate model, which is able to estimate both the cost function and the uncertainty of its own predictions. The methodology has been applied to a simplified use case: the design of a three-line simple catenary mooring system. Results show that BO is able to effectively arrive at an optimum solution while providing valuable information about the whole design space, demonstrating potential of the methodology to deal with uncertainties and enable informed decision-making from early design stages
Modeling COVID-19 dynamics in the Basque Country: characterizing population immunity profile from 2020 to 2022
Full text linkBackground: COVID-19, caused by SARS-CoV-2, has spread globally, presenting a signifcant public health challenge. Vaccination has played a critical role in reducing severe disease and deaths. However, the waning of immunity after vaccination and the emergence of immune-escape variants require the continuation of vaccination
efforts, including booster doses, to maintain population immunity. This study models the dynamics of COVID-19
in the Basque Country, Spain, aiming to characterize the population’s immunity profle and assess its impact
on the severity of outbreaks from 2020 to 2022.
Methods: A SIR/DS model was developed to analyze the interplay of virus-specifc and vaccine-induced immunity.
The model includes three levels of immunity, with boosting efects from reinfection and/or vaccination. It was validated using empirical daily case data from the Basque Country. The model tracks shifts in immunity status and their
effects on disease dynamics over time.
Results: The COVID-19 epidemic in the Basque Country progressed through three distinct phases, each shaped
by dynamic interactions between virus transmission, public health interventions, and vaccination eforts. The initial phase
was marked by a rapid surge in cases, followed by a decline due to strict public health measures, with a seroprevalence
of1.3%. In the intermediate phase, multiple smaller outbreaks emerged as restrictions were relaxed and new variants,
such as Alpha and Delta, appeared. During this period, reinfection rates reached 20%, and seroprevalence increased
to 32%. The final phase, dominated by the Omicron variant, saw a significant rise in cases driven by waning immunity
and the variant’s high transmissibility. Notably, 34% of infections during this phase occurred in the naive population,
with seroprevalence peaking at 43%. Across all phases, the infection of naive and unvaccinated individuals contributed
significantly to the severity of outbreaks, emphasizing the critical role of vaccination in mitigating disease impact.
Conclusion The findings underscore the importance of continuous monitoring and adaptive public health strategies to mitigate the evolving epidemiological and immunological landscape of COVID-19. Dynamic interactions
between immunity levels, reinfections, and vaccinations are critical in shaping outbreak severity and guiding evidence-based interventions
Machine learning based conformal predictors for uncertainty-aware compressive strength estimation of concrete
Full text linkEstimating concrete compressive strength is crucial for accurately predicting its performance, optimising material usage, and ensuring the durability and safety of the structure. Traditional machine learning (ML) models have primarily focused on deterministic predictions of compressive strength, often overlooking the uncertainty associated with these estimates. However, concrete is a non-homogeneous material with complex and variable behaviour, making it inherently difficult to predict compressive strength with precision. Therefore, incorporating uncertainty into predictive modelling is essential for producing more reliable and practical results in real-world engineering applications. This study addresses this gap by proposing a comprehensive framework for uncertainty quantification in concrete strength estimation using conformal prediction methods. In this comprehensive study, eight distinct machine learning models are systematically integrated with six conformal prediction variants to construct statistically rigorous prediction intervals. To evaluate the performance of the models holistically in engineering contexts, a novel Efficiency Score (ES) is proposed, combining empirical coverage, mean interval width, and point prediction accuracy. The findings reveal notable trade-offs between predicted interval width and empirical coverage across the model spectrum. Among the tested combinations, LightGBM coupled with Jackknife+ emerges as the most effective configuration, demonstrating the highest efficiency score. Additionally, conformal predictors exhibit satisfactory adaptation to heteroscedasticity, which arises in the predictions of higher-grade concrete (>40 MPa). Thus, the proposed framework empowers more informed decision-making in concrete design and quality control by providing robust uncertainty bounds advancing beyond traditional deterministic point predictions to support risk-aware infrastructure development
Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models
Full text linkTo model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states
Hierarchical compromise optimization of ambulance locations under stochastic travel times
No full textThe location of ambulances is a crucial strategic decision for Emergency Medical Services (EMS). The base stations must achieve efficient dispatching under the inherent uncertainty of emergency locations and travel times. Additionally, managers need decision-support models that incorporate the multi-objective nature of such an efficient system. This paper bridges the gap between these requirements by developing a multi-objective hierarchical compromise optimization framework under stochastic travel times. Our hierarchical compromise optimization approach leverages quasi-optimal coverage solutions to provide EMS managers with flexibility in balancing (a) minimal average response time, (b) maximal resource adequacy, and (c) minimal worst-case response times. The stochasticity of travel times is incorporated into the models using a methodology to estimate continuous probability distributions for available and non-available historical data. The proposed modeling induces cross-scenario constraints, which are computationally challenging as the problem size increases. We tackle this issue by presenting an ad-hoc extension of a primal scenario-decomposition algorithm that deals with such constraints. This extension achieves superior performance over state-of-the-art optimization software. Finally, we use real-world data from the Basque Public Healthcare System to test the framework and prove the managerial interest of the obtained results.PID2023-147410NB-100
PID2023-153222OB-I00
IT-1494-2
Antiampleness and ampleness of the Frobenius cokernel
No full textWe show that if is a smooth Fano variety containing a line or a conic
with respect to , then the Frobenius cokernel is not antiample; using
this criteria, we show that the only smooth Fano threefolds with antiample
Frobenius cokernel are and the quadric threefold (in
characteristic ), thus answering a question raised by Carvajal-Rojas
and Patakfalvi. We also show that for any smooth complete intersection
of degree such that or
, the Frobenius cokernel is not antiample. We also study the kernels of
the higher Cartier operators, and show that for and quadric
hypersurfaces, all the kernels of the higher Cartier operators are antiample,
and thus that the full set of kernels of the Cartier operators cannot
characterize projective space. Finally, we show that the Frobenius cokernel is
ample if and only if the cotangent bundle is ample.EUR2023-143443 funded by MCIN/AEI/10.13039/501100011033
PID2021-125052NA-I00, funded by MCIN/AEI/10.13039/50110001103
Non-existence of Radial Eigenfunctions for the Perturbed Heisenberg Sublaplacian
No full textWe prove uniform resolvent estimates in weighted L2-spaces for radial solutions of the sublaplacian on the Heisenberg group ℍd. The proofs are based on the multipliers methods, and strongly rely on the use of suitable multipliers and of the associated Hardy inequalities. The constants in our inequalities are explicit and depend only on the dimension d. As application of the method, we obtain some suitable smallness and repulsivity conditions on a complex radial potential V on ℍd such that +V has no radial eigenfunctions
ENDPOINT ESTIMATES FOR HIGHER ORDER MARCINKIEWICZ MULTIPLIERS
No full textWe consider Marcinkiewicz multipliers of any lacunary order defined by means of
uniformly bounded variation on each lacunary Littlewood–Paley interval of some fixed order
g ≥ 1. We prove the optimal endpoint bounds for such multipliers as a corollary of a more general
endpoint estimate for a class of multipliers introduced by Coifman, Rubio de Francia and
Semmes and further studied by Tao and Wright. Our methods also yield the best possible endpoint
mapping property for higher order Hörmander-Mihlin multipliers, namely multipliers
which are singular on every point of a lacunary set of order g. These results can be considered
as endpoint versions of corresponding results of Sjögren and Sjölin. Finally our methods
generalize a weak square function characterization of the space ! log1/2 ! in terms of a square
function introduced by Tao and Wright: we realize such a weak characterization as the dual of
the Chang–Wilson–Wolff inequality, thus giving corresponding weak square function characterizations
for the spaces ! logg/2 ! for general integer orders g ≥ 1
Semi-analytical framework for the study of finite-time stability of forced dynamical systems with slowly varying parameters
No full textFramework to analytically approximate the solution of forced dynamical systems with time varying parameters and to analyze their finite-time stability. The work was inspired by an example in robotic machining, where the mechanical parameters of the system can vary over a wide range during the process, and where there are large forces due to an assumed cutting operation. The simplest possible non-autonomous linear system undergoing dynamic stability loss is studied which serves as a solid foundation to explore the mathematical intricacy behind such systems. After defining the differential equation corresponding to this simple system, the complementary function is studied using a frozen-time approach. The particular integral can be evaluated for this system by the asymptotic expansion of error functions. We present a new approach for the approximation of particular integrals, the iterative integration by parts (IIBP) method, which is then extensively studied and compared to the equations describing the exact analytic solution. The convergence and sensitivity of the IIBP method are discussed. The method is extended to multiple degrees of freedom mechanical systems with time varying parameters. It is shown that standard numerical schemes are not suitable for predicting finite-time stability properties even in the simplest case, because small errors accumulate causing large differences from the analytical solution