arXiv subjects

María Dolores Ugarte

Publications and source records attributed to María Dolores Ugarte.

7 recordsLinked to original sources

arXiv2026

Are continuous models better than areal models for spatial disease mapping with a large number of small areas?

Classical methods for analysing areal mortality data typically rely on Poisson mixed models, which account for spatial and unstructured variability through prior distributions such as the BYM2 prior. This paper investigates alternative modeling approaches for settings involving a large number of small areas. Specifically, we explore the Stochastic Partial Differential Equation (SPDE) approach for continuous risk surfaces, examining an existing model from the literature alongside a proposed modification that incorporates an unstructured random effect. Finally, we evaluate the performance of both hierarchical mixed Poisson models and SPDE-based frameworks through a comprehensive simulation study across various spatial configurations and aggregation levels. Furthermore, we apply these approaches to the analysis of lung cancer mortality among men and women across Spanish municipalities during the period 2009--2022.

stat.ME↗

arXiv2026

Prior Smoothing for Multivariate Disease Mapping Models

To date, we have seen the emergence of a large literature on multivariate disease mapping. That is, incidence of (or mortality from) multiple diseases is recorded at the scale of areal units where incidence (mortality) across the diseases is expected to manifest dependence. The modeling involves a hierarchical structure: a Poisson model for disease counts (conditioning on the rates) at the first stage, and a specification of a function of the rates using spatial random effects at the second stage. These random effects are specified as a prior and introduce spatial smoothing to the rate (or risk) estimates. What we see in the literature is the amount of smoothing induced under a given prior across areal units compared with the observed/empirical risks. Our contribution here extends previous research on smoothing in univariate areal data models. Specifically, for three different choices of multivariate prior, we investigate both within prior smoothing according to hyperparameters and across prior smoothing. Its benefit to the user is to illuminate the expected nature of departure from perfect fit associated with these priors, providing measures of their smoothing behavior not captured by conventional measures of predictive performance. We propose both theoretical and empirical metrics for our investigation and illustrate with both simulated and real data.

stat.ME↗

arXiv2025

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence

Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly interested in methods for forecasting cancer incidence. However, in many countries there is an additional difficulty in projecting cancer incidence as regional registries are usually not established in the same year and therefore cancer incidence data series between different regions of a country are not harmonised over time. This study addresses the challenge of forecasting cancer incidence with incomplete data at both regional and national levels. To achieve this, we propose the use of multivariate spatio-temporal shared component models that jointly model mortality data and available cancer incidence data. We evaluate the performance of these multivariate models using lung cancer incidence data and the corresponding number of deaths reported in England for the period 2001-2019. Model performance was assessed using different predictive measures to select the best model.

stat.ME↗

arXiv2025

On prior smoothing with discrete spatial data in the context of disease mapping

Disease mapping attempts to explain observed health event counts across areal units, typically using Markov random field models. These models rely on spatial priors to account for variation in raw relative risk or rate estimates. Spatial priors introduce some degree of smoothing, wherein, for any particular unit, empirical risk or incidence estimates are either adjusted towards a suitable mean or incorporate neighbor-based smoothing. While model explanation may be the primary focus, the literature lacks a comparison of the amount of smoothing introduced by different spatial priors. Additionally, there has been no investigation into how varying the parameters of these priors influences the resulting smoothing. This study examines seven commonly used spatial priors through both simulations and real data analyses. Using areal maps of peninsular Spain and England, we analyze smoothing effects using two datasets with associated populations at risk. We propose empirical metrics to quantify the smoothing achieved by each model and theoretical metrics to calibrate the expected extent of smoothing as a function of model parameters. We employ areal maps in order to quantitatively characterize the extent of smoothing within and across the models as well as to link the theoretical metrics to the empirical metrics.

stat.ME↗

arXiv2024

Multivariate Bayesian models with flexible shared interactions for analyzing spatio-temporal patterns of rare cancers

Rare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components to enable a comprehensive analysis of both incidence and cancer mortality in rare cancer cases. These models allow the modulation of spatio-temporal effects between incidence and mortality, allowing for changes in their relationship over time. The new models have been implemented in INLA using r-generic constructions. We conduct a simulation study to evaluate the performance of the new spatio-temporal models. Our results show that multivariate spatio-temporal models incorporating a flexible shared spatio-temporal term outperform conventional multivariate spatio-temporal models that include specific spatio-temporal effects for each health outcome. We use these models to analyze incidence and mortality data for pancreatic cancer and leukaemia among males across 142 administrative health care districts of Great Britain over a span of nine biennial periods (2002-2019).

stat.ME↗

arXiv2024

Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences

Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km$^2$ region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.

cs.CV↗

arXiv2022

A Unique Cardiac Electrophysiological 3D Model

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about the heart diagnostic. Even though it is one of the major problems in biomedical research, an efficient mathematical formulation for this modelling has still not been found. In this paper, we present an outstanding mathematical model. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. The mathematical formulation is simple enough to be easily parametrized and rich enough to provide realistic signals. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram and vectocardiogram signals of any diseased or healthy heart, bringing together different systems in a single model. Furthermore, a novel algorithm accurately identifies the model parameters. This new discovery represents a revolution in electrocardiography research, solving one of the main problems in this field. It is especially useful for the automatic diagnosis of cardiovascular diseases, patient follow-up or decision-making on new therapies.

physics.med-ph↗