Review of spatio-temporal models for disease mapping

The EUROHEIS2 project (http://www.euroheis.org) is aimed at improving the analysis, reporting and dissemination of environmental health information. The project will further develop the health and environment information system for health threat analysis (the Rapid Inquiry Facility -- RIF) initiated in the previously funded EUROHEIS project. One of the specific objectives is to include spatio-temporal methods for disease mapping in the RIF. Statistical techniques for disease mapping have become very popular in public health analysis. These methods enable the smoothing of ecological health indicators accounting for the geographical structure of the units under study. As a consequence, more reliable risk estimates in less populated areas are obtained due to the sharing of information between neighbouring regions, which are assumed to share common risk factors. In this way, it becomes possible to display the geographical distribution of risk even in small areas. But disease risks are variable in space and time, and supporting risk management should ideally incorporate spatio-temporal analysis tools. Recently, several spatio-temporal disease mapping techniques have been proposed. However, the implementation of these methods is not always easy or adequate for a quick response tool. Furthermore, there is not a wide consensus on how to describe temporal and spatial evolution at the same time in a proper way. Therefore, a special effort is necessary to indentify which methods are suitable for inclusion in the RIF

Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields

In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace approximation (INLA) with the stochastic partial differential equation (SPDE) approach facilitates the handling of large datasets in excellent computation times. Our approach allows the evaluation of different sampling strategies, from which we obtain inferences and prediction maps with similar behaviour to those obtained when we consider all subjects in the study population. The analysis of the different sampling strategies allows us to recognize the relevance of spatial components in the studied phenomenon. We demonstrate how Bayesian kriging can incorporate sources of uncertainty associated with the prediction parameters, which leads to more realistic and accurate estimation of the uncertainty. We illustrate the methodology with samplings of Citrus macrophylla affected by the tristeza virus (CTV) grown in a nursery

Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model

Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisation techniques represent a suitable approach for modelling such complex patterns of variability. In this work, we present a hierarchical factorisation model conceived from a systems biology point of view. The model integrates the topology of molecular pathways, allowing to simultaneously factorise genes and pathways activity matrices. The protocol was evaluated by using simulations, showing a high degree of accuracy. Furthermore, the analysis with a real cohort of breast cancer patients depicted the internal composition of some of the most relevant altered biological processes in the disease, describing gene and pathway level strategies and their observed combinations in the population of patients. We envision that this kind of approaches will be essential to better understand the hallmarks of cancer.Peer ReviewedPostprint (published version

The Integrated nested Laplace approximation for fitting models with multivariate response

This paper introduces a Laplace approximation to Bayesian inference in regression models for multivariate response variables. We focus on Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data. These data, which mainly consist of proportions or percentages of disjoint categories, are widely known as compositional data and are common in areas such as ecology, geology, and psychology. We provide both the theoretical foundations and a description of how this Laplace approximation can be implemented in the case of Dirichlet regression. The paper also introduces the package dirinla in the R-language that extends the INLA package, which can not deal directly with multivariate likelihoods like the Dirichlet likelihood. Simulation studies are presented to validate the good behaviour of the proposed method, while a real data case-study is used to show how this approach can be applied

Climatic distribution of citrus black spot caused by 'Phyllosticta citricarpa'. A historical analysis of disease spread in South Africa

Citrus black spot (CBS), caused by Phyllosticta citricarpa, is one of the main fungal diseases of citrus worldwide. The Mediterranean Basin is free of the disease and thus phytosanitary measures are in place to avoid the entry of P. citricarpa in the EU territory. However, the suitability of the climates present in the Mediterranean Basin for CBS establishment and spread is debated. As a case study, an analysis of climate types and environmental variables in South Africa was conducted to identify potential associations with CBS distribution. The spread of the disease was traced and georeferenced datasets of CBS distribution and environmental variables were assembled. In 1950 CBS was still confined to areas of temperate climates with summer rainfall (Cw, Cf), but spread afterwards to neighbouring regions with markedly drier conditions. Actually, the hot arid steppe (BSh) is the predominant climate where CBS develops in South Africa nowadays. The disease was not detected in the Mediterranean-type climates Csa and Csb as defined by the Koppen-Geiger system and the more restrictive Aschmann's classification criteria. However, arid steppe (BS) climates, where CBS is prevalent in South Africa, are common in important citrus areas in the Mediterranean Basin. The most noticeable change in the environmental range occupied by CBS in South Africa was the amount and seasonality of rainfall. Due to the spread of the disease to dryer regions, the minimum annual precipitation in CBS-affected areas declined from 663 mm in 1950 to 339 mm at present. The minimum value precipitation of warmest quarter also declined from 290 to 96 mm. Strong spatial autocorrelation in CBS distribution data was detected, so further modelling efforts should consider the relative contribution of environmental variables and spatial effects to estimate the potential geographical range of CBS

Criteria for efficient prevention of dissemination and successful eradication of Erwinia amylovora (the cause of fire blight) in Aragón, Spain

Erwinia amylovora was detected on pome fruits in the Aragón region (North-Eastern Spain), in a ca. 5 km radius area located in the mid Jalón river (mid Ebro Valley) in the province of Zaragoza, during 2000‒2003. Eight years have now passed since this pathogen was last detected, without new infections being reported in the same area. The bases for surveys and rapid eradication performed have been analyzed in detail to understand the reasons for the success in removing fireblight. The results demonstrate that intensive surveillance, risk assessment, plant analyses using accurate identification methods, and, especially, rapid total or selective eradication of infected trees in the plots have been very effective in preventing the generalized spread of fireblight and in delaying economic losses associated with this disease. Eradication and compensation to growers, estimated to cost approx. € 467,000, were clearly counterbalanced by the economic value of apple and pear production in the 2000‒2003 period (approx. € 368 million). Fire blight risk-assessment, using the MARYBLYT system, showed that climatic conditions in the studied area were favourable to infections during the analyzed period (1997‒2006). Molecular characterization of E. amylovora strains had revealed their homogeneity, suggesting that these fire blight episodes could have been caused by just one inoculum source, supporting the hypothesis that there was a unique introduction of E. amylovora in the studied area. Spatial spread of E. amylovora to trees was analyzed within six orchards, indicating an aggregated distribution model. This Spanish experience demonstrates the success of scientifically-based prevention methods that lead to the deployment of a fast and strict containment strategy, useful for other Mediterranean areassurveysrisk-assessmentspatial analysisstrain characterizationPublishe

Almond Anthracnose: Current Knowledge and Future Perspectives

Almond anthracnose caused by Colletotrichum spp. has been described as one of the most important diseases of this nut crop in the main almond-growing regions worldwide, including California, Australia and Spain. Currently, almond anthracnose is considered a re-emerging disease in the countries across the Mediterranean Basin due to the shift of plantations from the original crop areas to others with climatic, edaphic and orographic conditions favoring crop growing and yield. The pathogen mainly affects fruit at the youngest maturity stages, causing depressed, round and orange or brown lesions with abundant gum. The affected fruits can fall prematurely and lead to the drying of branches, causing significant economic losses in years of epidemics. This review aims to compile the current knowledge on the etiology, epidemiology and management of this disease