BUSCADOR RECOLECTA

Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain., Adin, A., Goicoa, T. and Ugarte, M.D. research has been supported by the Spanish Ministry of Economy and Competitiveness (project MTM2014-51992-R), by the Spanish Ministry of Economy, Industry, and Competitiveness (project MTM2017-82553-R jointly nanced with the European Regional Development Fund -FEDER-). Lee, D. research has been supported by the UK Medical Research Council (Grant number MR/L022184/1).

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions., This research was supported by the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R), the Government of Navarre (Project PI015, 2016), and by the Fundación Caja Navarra-UNED Pamplona (2016).

Background: Brain cancer incidence rates in Spain are below the European’s average. However, there are two
regions in the north of the country, Navarre and the Basque Country, ranked among the European regions with the
highest incidence rates for both males and females. Our objective here was two-fold. Firstly, to describe the temporal
evolution of the geographical pattern of brain cancer incidence in Navarre and the Basque Country, and secondly, to
look for specific high risk areas (municipalities) within these two regions in the study period (1986–2008).
Methods: A mixed Poisson model with two levels of spatial effects is used. The model also included two levels of
spatial effects (municipalities and local health areas). Model fitting was carried out using penalized quasi-likelihood.
High risk regions were detected using upper one-sided confidence intervals.
Results: Results revealed a group of high risk areas surrounding Pamplona, the capital city of Navarre, and a few
municipalities with significant high risks in the northern part of the region, specifically in the border between Navarre
and the Basque Country (Gipuzkoa). The global temporal trend was found to be increasing. Differences were also
observed among specific risk evolutions in certain municipalities.
Conclusions: Brain cancer incidence in Navarre and the Basque Country (Spain) is still increasing with time. The
number of high risk areas within those two regions is also increasing. Our study highlights the need of continuous
surveillance of this cancer in the areas of high risk. However, due to the low percentage of cases explained by the
known risk factors, primary prevention should be applied as a general recommendation in these populations., This research has been supported by the Spanish Ministry of Science and Innovation (project MTM 2011-22664, jointly sponsored with FEDER grants and project MTM2014-51992-R), and by the Health Department of the Navarre Government (project 113, Res.2186/2014).

Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on
generalized linear mixed models (GLMMs) including fixed
effects and spatial, temporal, and spatio-temporal random
effects. These GLMMs are typically not identifiable and
constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes
lead to misleading results. In particular, the penalized
quasi-likelihood fitting technique automatically centers the
random effects even when this is not necessary. In the
Bayesian approach, the recently-introduced integrated
nested Laplace approximations computing technique can
also produce wrong results if constraints are not wellspecified. In this paper the spatial, temporal, and spatiotemporal interaction random effects are reparameterized
using the spectral decompositions of their precision
matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to
illustrate the ideas., This work has been supported by the Spanish
Ministry of Economy and Competitiveness (project MTM2014-
51992-R), and by the Health Department of the Navarre Government
(Project 113, Res.2186/2014).

In this paper age-space-time models based on one and two-dimensional P-splines with
B-spline bases are proposed for smoothing mortality rates, where both xed relative scale
and scale invariant two-dimensional penalties are examined. Model tting and inference
are carried out using integrated nested Laplace approximations (INLA), a recent Bayesian
technique that speeds up computations compared to McMC methods. The models will be
illustrated with Spanish breast cancer mortality data during the period 1985-2010, where a
general decline in breast cancer mortality has been observed in Spanish provinces in the last
decades. The results reveal that mortality rates for the oldest age groups do not decrease in
all provinces., This work has been supported by the Spanish Ministry of Economy and Competitiveness (project
MTM2014-51992-R), and by the Health Department of the Navarre Government (Project 113,
Res.2186/2014).

Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference., This work was supported by grants from the Spanish Ministry of Economy and Competitiveness (projects MTM2014-51992-R-MDU- and MTM2016-77501-P -ALQ-, jointly financed with the European Regional Development Fund), the Spanish Ministry of Economy, Industry, and Competitiveness (MTM2017-82553-R jointly financed with the European Regional Development Fund (FEDER). MDU, AA), and the Colombian Administrative Department of Science and Technology (grant 646-2014 for doctoral studies abroad) DAMB.

Recently, the interest in studying pancreatic cancer mortality has increased due to its high
lethality. In this work a detailed analysis of pancreatic cancer mortality in Spanish provinces
was performed using recent data. A set of multivariate spatial gender-age-period-cohort
models was considered to look for potential candidates to analyze pancreatic cancer mortality
rates. The selected model combines features of APC (age-period-cohort) models with
disease mapping approaches. To ensure model identifiability sum-to-zero constraints were
applied. A fully Bayesian approach based on integrated nested Laplace approximations
(INLA) was considered for model fitting and inference. Sensitivity analyses were also conducted.
In general, estimated average rates by age, cohort, and period are higher in males
than in females. The higher differences according to age between males and females correspond
to the age groups [65, 70), [70, 75), and [75, 80). Regarding the cohort, the greatest
difference between men and women is observed for those born between the forties and the
sixties. From there on, the younger the birth cohort is, the smaller the difference becomes.
Some cohort differences are also identified by regions and age-groups. The spatial pattern
indicates a North-South gradient of pancreatic cancer mortality in Spain, the provinces in
the North being the ones with the highest effects on mortality during the studied period.
Finally, the space-time evolution shows that the space pattern has changed little over time., This work was supported by Spanish Ministry of Economy and Competitiveness (Project MTM 2014-51992-R) (Ugarte, Etxeberria, Goicoa) and Health Department of the Navarre Government (Project 113, Res.2186/2014) (Ugarte, Etxeberria, Goicoa).

El principal objetivo de esta tesis es la introducción y desarrollo de métodos estadísticos en imágenes satelitales para mejorar el procesamiento, suavizado, predicción, e inferencia de los datos de teledetección. Este objetivo principal se puede dividir en los siguientes sub-objetivos. El primero contempla la adquisición, gestión, y automatización los procesos de descarga de datos de teledetección desde múltiples plataformas de manera estandarizada. El segundo es proporcionar una breve descripción de las principales herramientas geostadísticas utilizadas en teledetección, enfatizando la importancia de los métodos estocásticos espacio-temporales. El tercer sub-objetivo consiste en explorar algunas técnicas para detectar cambios de tendencia, analizando la evolución natural de algunos índices. El cuarto subobjetivo es el desarrollo de nuevos métodos para la predicción de datos perdidos y suavización de errores en imágenes satelitales utilizando la dependencia espacial y temporal. El objetivo final es el desarrollo de un nuevo paquete de R llamado ‘RGISTools’. Permite la descarga, pre-procesamiento, y gestión de imágenes satelitales de Landsat, MODIS, y Sentinel-2. También contiene los nuevos métodos de predicción de datos perdidos y suavización derivados de esta tesis., The main objective of this thesis is the introduction and development of statistical methods in satellite imagery to improve the processing, smoothing, prediction, and inference of remote sensing data. This objective can be split into the following sub-objectives. The first one is acquiring, managing, and automatizing processes to download remote sensing data from different platforms in a standardised way. The second one is to provide a brief review of the main geostatistics tools used in satellite imagery, emphasizing the importance of considering stochastic spatiotemporal methods. The third sub-objective consists in exploring some techniques to detect trend changes when analysing the natural evolution of certain indices. The four goal is to develop new methods for filling gaps and smoothing errors in satellite images using spatial and temporal dependence. As a final goal a new R package, called 'RGISTools', was created. It allows downloading, pre-processing, and managing Landsat, Modis, and Sentinel-2 satellite images. It also contains the new gap filling and smoothing methods derived in this thesis., Financial support of three institutions: a) the Spanish Ministry of Economy and Competitiveness (project MTM2017-82553-R AEI/FEDER grants,
MTM2014-51992-R, and MTM2011-22664), b) the Government of Navarre (projects PI015-2016 and PI043-2017), and c) 'La Caixa' Foundation (ID 1000010434), Caja
Navarra Foundation and UNED Pamplona, under agreement LCF/PR/PR15/51100007., Programa de Doctorado en Ciencias y Tecnologías Industriales (RD 99/2011), Industria Zientzietako eta Teknologietako Doktoretza Programa (ED 99/2011)