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It is our primary focus to study the spatial distribution of disease incidence at different geographical levels. Often, spatial data are available in the form of aggregation at multiple scale levels such as census tract, county, state, and so on. When data are aggregated from a fine (e.g. county) to a coarse (e.g. state) geographical level, there will be loss of information. The problem is more challenging when excessive zeros are available at the fine level. After data aggregation, the excessive zeros at the fine level will be reduced at the coarse level. If we ignore the zero inflation and the aggregation effect, we could get inconsistent risk estimates at the fine and coarse levels. Hence, in this paper, we address those problems using zero inflated multiscale models that jointly describe the risk variations at different geographical levels. For the excessive zeros at the fine level, we use a zero inflated convolution model, whereas we consider a regular convolution model for the smoothed data at the coarse level. These methods provide a consistent risk estimate at the fine and coarse levels when high percentages of structural zeros are present in the data.

Chronic activation of coagulation is one of the features of sickle cell disease (SCD). Increased tissue factor expression, phosphatidylserine exposure, thrombin generation and fibrinolysis, as well as decreased levels of natural anticoagulants have been reported in SCD patients and in the mouse models of SCD. Consistent with this, patients with SCD are prone to develop thrombotic complications. In addition, the altered morphology of sickle red blood cells (RBC) may also alter the properties and dynamics of clot formation in SCD patients. Clinical data and results from animal models have revealed complex interactions between coagulation, chronic hemolysis, and inflammation suggesting that activation of coagulation may contribute to the pathophysiology of SCD.

In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.

Aim: Carotid artery disease (CAD) is an atherosclerotic inflammatory disease that affects the arterial wall, specifically at points of bifurcation where blood flow is disturbed. Abnormal blood rheology could participate in the pathophysiology of ischemic cardiovascular disease. Physical activity (PA) is known to improve blood rheology in several chronic disorders. This study aims to (i) compare the hemorheological profile of CAD patients and controls and (ii) investigate the associations between daily PA and hemorheological parameters in CAD patients. Methods: Blood viscosity, red blood cell (RBC) aggregation and RBC deformability were assessed in 80 patients (15 symptomatic and 65 asymptomatic) and 14 age-matched controls. Patients' PA levels were evaluated using questionnaires. Results: Symptomatic patients showed increased blood viscosity and RBC aggregation compared to healthy controls. RBC aggregation was significantly lower in the most physically active patients compared to the least physically active ones. Blood viscosity and RBC deformability did not vary according to physical activity level. Conclusions: Our results showed greater hemorheological abnormalities (blood hyper-viscosity and hyper-aggregation of red blood cells) in the most severe CAD patients, which could exacerbate the risk of stroke in patients with stenosis. As the most physically active patients had lower RBC aggregation than those who were less physically active, it is possible that regular PA may limit hemorheological alterations in CAD patients.

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application., Competing Interests: Conflict of Interest The authors declare no conflict of interest.

In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.

In many follow-up studies different types of outcomes are collected including longitudinal measurements and time-to-event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. However, in many studies several longitudinal biomarkers are of interest and instead of selecting one single biomarker, the relationships between all these outcomes and their association with survival needs to be investigated. Our motivating study comes from Peritoneal Dialysis Programme in Nephrology research from Nephrology Unit, CHP (Hospital de Santo António), Porto, Portugal in which the interest relies on the possible association between various biomarkers (calcium, phosphate, parathormone, and creatinine) and the patients' survival. To this aim, we propose a two-stage model-based approach for multivariate longitudinal and survival data that allowed us to study such complex association structure. The multivariate model suggested in this paper provided new insights in the area of nephrology research showing valid results in comparison with those models studying each longitudinal biomarker with survival separately., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

This paper formulates and compares a general class of spatiotemporal models for univariate space-time geostatistical data. The implementation of stochastic partial differential equation (SPDE) approach combined with integrated nested Laplace approximation into the R-INLA package makes it computationally feasible to use spatiotemporal models. However, the impact of specifying models with and without space-time interaction is unclear. We formulate an extensive class of additive and coupled spatiotemporal SPDE models and investigate the distinction between them by (1) Extending their temporal effect, allowing a random walk process in time, (2) varying the spatial correlation function and (3) running a simulation study to assess the effect of misspecifying the spatial and temporal models, and to assess the generalizability of our results to a higher number of locations. Our methods are illustrated with Culicoides data from Belgium. The Bayesian spatial predictions showed that the highest prevalence of Culicoides species was found in the Northeastern and central parts of Belgium during summer., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Spatial smoothing models play an important role in the field of small area estimation. In the context of complex survey designs, the use of design weights is indispensable in the estimation process. Recently, efforts have been made in these spatial smoothing models, in order to obtain reliable estimates of the spatial trend. However, the concept of missing data remains a prevalent problem in the context of spatial trend estimation as estimates are potentially subject to bias. In this paper, we focus on spatial health surveys where the available information consists of a binary response and its associated design weight. Furthermore, we investigate the impact of nonresponse as missing data on a range of spatial models for different missingness mechanisms and different degrees of missingness by means of an extensive simulation study. The computations were performed in R, using INLA and other existing packages. The results show that weight adjustment to correct for missingness has a beneficial effect on the bias in the missing at random setting for all models. Furthermore, we estimate the geographical distribution of perceived health at the district level based on the Belgian Health Interview Survey (2001). Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background: Sickle cell anaemia (SCA) is a severe hereditary haemoglobinopathy characterised by haemorheological abnormalities, which play a role in the occurrence of several acute and chronic clinical complications. While β S -haplotypes and alpha-thalassaemia modulate SCA clinical severity, their effects on blood rheology have been incompletely described. The aim of this study was to test the effects of these genetic modifiers on the haemorheological properties and clinical complication of children with SCA., Procedure: Steady-state haemorheological profile, biological parameters, β S -haplotypes, alpha-globin status, vaso-occlusive crisis (VOC) and acute chest syndrome frequencies were analysed in 128 children (aged 5 to 18 years) with SCA., Results: Patients with alpha-thalassaemia showed increased red blood cell (RBC) deformability and aggregation compared to those without. Median VOC rate was higher in patients with homozygous alpha-thalassaemia compared to those with a normal alpha genotype. Conversely, the haemorheological profile and clinical complications were not influenced by the β S -haplotypes in our study., Conclusion: Our results demonstrate that alpha-thalassaemia is associated with higher risk for VOC events in children with SCA, which may be due in part to its effects on RBC deformability and aggregation., (© 2017 Wiley Periodicals, Inc.)

In spatial epidemiology, data are often arrayed hierarchically. The classification of individuals into smaller units, which in turn are grouped into larger units, can induce contextual effects. On the other hand, a scaling effect can occur due to the aggregation of data from smaller units into larger units. In this paper, we propose a shared multilevel model to address the contextual effects. In addition, we consider a shared multiscale model to adjust for both scale and contextual effects simultaneously. We also study convolution and independent multiscale models, which are special cases of shared multilevel and shared multiscale models, respectively. We compare the performance of the models by applying them to real and simulated data sets. We found that the shared multiscale model was the best model across a range of simulated and real scenarios as measured by the deviance information criterion (DIC) and the Watanabe Akaike information criterion (WAIC)., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Compared to other cyclooxygenase-2 inhibitors, celecoxib is associated with a lower cardiovascular risk, though the mechanism remains unclear. Angiotensin II is an important mediator of oxidative stress in the pathophysiology of vascular disease. Cyclooxygenase-2 may modify the effects of angiotensin II though this has never been studied in humans. The purpose of the study was to test the effects of selective cyclooxygenase-2 inhibition on plasma measures of oxidative stress, the vasoconstrictor endothelin-1, and nitric oxide metabolites, both at baseline and in respose to Angiotensin II challenge in healthy humans. Measures of 8-hydroxydeoxyguanosine, advanced oxidation protein products, nitrotyrosine, endothelin-1, and nitric oxide metabolites were assessed from plasma samples drawn at baseline and in response to graded angiotensin II infusion (3 ng/kg/min × 30 min, 6 ng/kg/min × 30 min) before and after 14 days of cyclooxygenase-2 inhibition in 14 healthy subjects (eight male, six female) in high salt balance, a state of maximal renin angiotensin system suppression. Angiotensin II infusion significantly increased plasma oxidative stress compared to baseline (8-hydroxydeoxyguanosine; +17%; advanced oxidation protein products; +16%), nitrotyrosine (+76%). Furthermore, levels of endothelin-1 levels were significantly increased (+115%) and nitric oxide metabolites were significantly decreased (-20%). Cycloxygenase-2 inhibition significantly limited the increase in 8-hydroxydeoxyguanosine, nitrotyrosine and the decrease in nitric oxide metabolites induced by angiotensin II infusion, though no changes in advanced oxidation protein products and endothelin-1 concentrations were observed. Cyclooxygenase-2 inhibition with celecoxib partially limited the angiotensin II-mediated increases in markers of oxidative stress in humans, offering a potential physiological pathway for the improved cardiovascular risk profile of this drug.

Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Statistical analysis of count data typically starts with a Poisson regression. However, in many real-life applications, it is observed that the variation in the counts is larger than the mean, and one needs to deal with the problem of overdispersion in the counts. Several factors may contribute to overdispersion: (1) unobserved heterogeneity due to missing covariates, (2) correlation between observations (such as in longitudinal studies), and (3) the occurrence of many zeros (more than expected from the Poisson distribution). In this paper, we discuss a model that allows one to explicitly take each of these factors into consideration. The aim of this paper is twofold: (1) investigate whether we can identify the cause of overdispersion via model selection, and (2) investigate the impact of a misspecification of the model on the power of a covariate. The paper is motivated by a study of the occurrence of drug-induced arrhythmia in beagle dogs based on electrocardiogram recordings, with the objective to evaluate the effect of potential drugs on the heartbeat irregularities. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Purpose: Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables., Methods: In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina., Results: Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation., Conclusions: Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion., Methods: The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature., Results: The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary., Conclusions: Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Spatio-temporal analysis of small area health data often involves choosing a fixed set of predictors prior to the final model fit. In this paper, we propose a spatio-temporal approach of Bayesian model selection to implement model selection for certain areas of the study region as well as certain years in the study time line. Here, we examine the usefulness of this approach by way of a large-scale simulation study accompanied by a case study. Our results suggest that a special case of the model selection methods, a mixture model allowing a weight parameter to indicate if the appropriate linear predictor is spatial, spatio-temporal, or a mixture of the two, offers the best option to fitting these spatio-temporal models. In addition, the case study illustrates the effectiveness of this mixture model within the model selection setting by easily accommodating lifestyle, socio-economic, and physical environmental variables to select a predominantly spatio-temporal linear predictor.

Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts.

We consider models for hierarchical count data, subject to overdispersion and/or excess zeros. Molenberghs et al. () and Molenberghs et al. () extend the Poisson-normal generalized linear-mixed model by including gamma random effects to accommodate overdispersion. Excess zeros are handled using either a zero-inflation or a hurdle component. These models were studied by Kassahun et al. (). While flexible, they are quite elaborate in parametric specification and therefore model assessment is imperative. We derive local influence measures to detect and examine influential subjects, that is subjects who have undue influence on either the fit of the model as a whole, or on specific important sub-vectors of the parameter vector. The latter include the fixed effects for the Poisson and for the excess-zeros components, the variance components for the normal random effects, and the parameters describing gamma random effects, included to accommodate overdispersion. Interpretable influence components are derived. The method is applied to data from a longitudinal clinical trial involving patients with epileptic seizures. Even though the data were extensively analyzed in earlier work, the insight gained from the proposed diagnostics, statistically and clinically, is considerable. Possibly, a small but important subgroup of patients has been identified., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by mixing spatially varying probability weights of correlated (CH) and uncorrelated heterogeneities (UH). The first model assumes that there is no linkage between the different scales and, hence, we consider independent mixture convolution models at each scale. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both CH and UH simultaneously. We applied these models to real and simulated data, and found that the fourth model is the best model followed by the second model., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

To describe the spatial distribution of diseases, a number of methods have been proposed to model relative risks within areas. Most models use Bayesian hierarchical methods, in which one models both spatially structured and unstructured extra-Poisson variance present in the data. For modelling a single disease, the conditional autoregressive (CAR) convolution model has been very popular. More recently, a combined model was proposed that 'combines' ideas from the CAR convolution model and the well-known Poisson-gamma model. The combined model was shown to be a good alternative to the CAR convolution model when there was a large amount of uncorrelated extra-variance in the data. Less solutions exist for modelling two diseases simultaneously or modelling a disease in two sub-populations simultaneously. Furthermore, existing models are typically based on the CAR convolution model. In this paper, a bivariate version of the combined model is proposed in which the unstructured heterogeneity term is split up into terms that are shared and terms that are specific to the disease or subpopulation, while spatial dependency is introduced via a univariate or multivariate Markov random field. The proposed method is illustrated by analysis of disease data in Georgia (USA) and Limburg (Belgium) and in a simulation study. We conclude that the bivariate combined model constitutes an interesting model when two diseases are possibly correlated. As the choice of the preferred model differs between data sets, we suggest to use the new and existing modelling approaches together and to choose the best model via goodness-of-fit statistics. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates., (© The Author(s) 2016.)

Objectives: Blood rheology plays a key role in the pathophysiology of sickle cell anaemia (SS) and sickle cell haemoglobin C disease (SC), but its evolution over the lifespan is unknown., Materials and Methods: Blood viscosity, red blood cell (RBC) deformability and aggregation, foetal haemoglobin (HbF) and haematocrit were measured in 114 healthy individuals (AA), 267 SS (161 children + 106 adults) and 138 SC (74 children + 64 adults) patients., Results: Our results showed that 1) RBC deformability is at its maximal value during the early years of life in SS and SC populations, mainly because HbF level is also at its peak, 2) during childhood and adulthood, hydroxycarbamide treatment, HbF level and gender modulated RBC deformability in SS patients, independently of age, 3) blood viscosity is higher in older SS and SC patients compared to younger ones and 4) haematocrit decreases as SS patients age., Conclusion: The hemorheological changes detected in older patients could play a role in the progressive development of several chronic disorders in sickle cell disease, whose prevalence increases with age. Retarding these age-related haemorheological impairments, by using suitable drugs, may minimize the risks of vaso-occlusive events and chronic disorders.

In chemical risk assessment, it is important to determine the quantiles of the distribution of concentration data. The selection of an appropriate distribution and the estimation of particular quantiles of interest are largely hindered by the omnipresence of observations below the limit of detection, leading to left-censored data. The log-normal distribution is a common choice, but this distribution is not the only possibility and alternatives should be considered as well. Here, we focus on several distributions that are related to the log-normal distribution or that are seminonparametric extensions of the log-normal distribution. Whereas previous work focused on the estimation of the cumulative distribution function, our interest here goes to the estimation of quantiles, particularly in the left tail of the distribution where most of the left-censored data are located. Two different model averaged quantile estimators are defined and compared for different families of candidate models. The models and methods of selection and averaging are further investigated through simulations and illustrated on data of cadmium concentration in food products. The approach is extended to include covariates and to deal with uncertainty about the values of the limit of detection. These extensions are illustrated with (134) cesium measurements from Fukushima Prefecture, Japan. We can conclude that averaged models do achieve good performance characteristics in case no useful prior knowledge about the true distribution is available; that there is no structural difference in the performance of the direct and indirect method; and that, not surprisingly, only the true or closely approximating model can deal with extremely high percentages of censoring., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: The Ebola outbreak in West Africa has infected at least 27,443 individuals and killed 11,207, based on data until 24 June, 2015, released by the World Health Organization (WHO). This outbreak has been characterised by extensive geographic spread across the affected countries Guinea, Liberia and Sierra Leone, and by localized hotspots within these countries. The rapid recognition and quantitative assessment of localised areas of higher transmission can inform the optimal deployment of public health resources., Methods: A variety of mathematical models have been used to estimate the evolution of this epidemic, and some have pointed out the importance of the spatial heterogeneity apparent from incidence maps. However, little is known about the district-level transmission. Given that many response decisions are taken at sub-national level, the current study aimed to investigate the spatial heterogeneity by using a different modelling framework, built on publicly available data at district level. Furthermore, we assessed whether this model could quantify the effect of intervention measures and provide predictions at a local level to guide public health action. We used a two-stage modelling approach: a) a flexible spatiotemporal growth model across all affected districts and b) a deterministic SEIR compartmental model per district whenever deemed appropriate., Findings: Our estimates show substantial differences in the evolution of the outbreak in the various regions of Guinea, Liberia and Sierra Leone, illustrating the importance of monitoring the outbreak at district level. We also provide an estimate of the time-dependent district-specific effective reproduction number, as a quantitative measure to compare transmission between different districts and give input for informed decisions on control measures and resource allocation. Prediction and assessing the impact of control measures proved to be difficult without more accurate data. In conclusion, this study provides us a useful tool at district level for public health, and illustrates the importance of collecting and sharing data.

Purpose: Many types of cancer have an underlying spatial incidence distribution. Spatial model selection methods can be useful when determining the linear predictor that best describes incidence outcomes., Methods: In this article, we examine the applications and benefits of using two different types of spatial model selection techniques, Bayesian model selection and Bayesian model averaging, in relation to colon cancer incidence in the state of Georgia, United States., Results: Both methods produce useful results that lead to the determination that median household income and percent African American population are important predictors of colon cancer incidence in the Northern counties of the state, whereas percent persons below poverty level and percent African American population are important in the Southern counties., Conclusions: Of the two presented methods, Bayesian model selection appears to provide more succinct results, but applying the two in combination offers even more useful information into the spatial preferences of the alternative linear predictors., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Red blood cell (RBC) deformability is severely decreased in patients with sickle cell anemia (SCA), which plays a role in the pathophysiology of the disease. However, investigation of RBC deformability from SCA patients demands careful methodological considerations. We assessed RBC deformability by ektacytometry (LORRCA MaxSis, Mechatronics, The Netherlands) in 6 healthy individuals and 49 SCA patients and tested the effects of different heights of the RBC diffraction patterns, obtained by altering the camera gain of the LORRCA, on the result of RBC deformability measurements, expressed as Elongation Index (EI). Results indicate that the pattern of RBCs from control subjects adopts an elliptical shape under shear stress, whereas the pattern of RBCs from individuals with SCA adopts a diamond shape arising from the superposition of elliptical and circular patterns. The latter represent rigid RBCs. While the EI measures did not change with the variations of the RBC diffraction pattern heights in the control subjects, we observed a decrease of EI when the RBC diffraction pattern height is increased in the SCA group. The differences in SCA EI values measured at 5 Pa between the different diffraction pattern heights correlated with the percent of hemoglobin S and the percent of sickled RBC observed by microscopy. Our study confirms that the camera gain or aperture of the ektacytometer should be used to standardize the size of the RBC diffraction pattern height when measuring RBC deformability in sickle cell patients and underscores the potential clinical utility of this technique.

Objective: It is predicted that Africa will have the greatest increase in the number of patients with type 2 diabetes mellitus (T2DM) within the next decade. T2DM patients are at risk for cardiovascular disorders. In Sub-Saharan African countries, sickle cell trait (SCT) is frequent. Despite the presence of modest abnormalities in hemorheology and oxidative stress, SCT is generally considered a benign condition. Little is known about vascular function in SCT, although recent studies demonstrated an increased risk of cardiovascular disorders, including venous thromboembolism, stroke, and chronic kidney disease. We hypothesized that SCT could accentuate the vascular dysfunction observed in T2DM., Research Design and Methods: The current study, conducted in Senegal, compared vascular function, hemorheological profile, and biomarkers of oxidative stress, inflammation, and nitric oxide metabolism in healthy individuals (CONT), subjects with T2DM or SCT, and patients with both T2DM and SCT (T2DM-SCT)., Results: Flow-mediated dilation was blunted in individuals with T2DM, SCT, and T2DM-SCT compared with CONT, with vascular dysfunction being most pronounced in the latter group. Carotid-femoral pulse wave velocity measurements demonstrated increased arterial stiffness in T2DM-SCT. Oxidative stress, advanced glycation end products, and inflammation (interleukin-1β) were greater in patients with T2DM-SCT compared with the other groups. Blood viscosity was higher in individuals with TD2M, SCT carriers, and individuals with T2DM-SCT, and the values were further increased in the latter group., Conclusions: Our results demonstrate severe biological abnormalities and marked vascular dysfunction in patients with both T2DM and SCT. SCT should be viewed as a risk factor for further cardiovascular disorders in individuals with T2DM., (© 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs., (Copyright © 2015 John Wiley & Sons, Ltd.)

Many human infectious diseases originate from animals or are transmitted through animal vectors. We aimed to identify factors that are predictive of ownership and touching of animals, assess whether animal ownership influences social contact behavior, and estimate the probability of a major zoonotic outbreak should a transmissible influenza-like pathogen be present in animals, all in the setting of a densely populated European country. A diary-based social contact survey (n = 1768) was conducted in Flanders, Belgium, from September 2010 until February 2011. Many participants touched pets (46%), poultry (2%) or livestock (2%) on a randomly assigned day, and a large proportion of participants owned such animals (51%, 15% and 5%, respectively). Logistic regression models indicated that larger households are more likely to own an animal and, unsurprisingly, that animal owners are more likely to touch animals. We observed a significant effect of age on animal ownership and touching. The total number of social contacts during a randomly assigned day was modeled using weighted-negative binomial regression. Apart from age, household size and day type (weekend versus weekday and regular versus holiday period), animal ownership was positively associated with the total number of social contacts during the weekend. Assuming that animal ownership and/or touching are at-risk events, we demonstrate a method to estimate the outbreak potential of zoonoses. We show that in Belgium animal-human interactions involving young children (0-9 years) and adults (25-54 years) have the highest potential to cause a major zoonotic outbreak.

This paper deals with the analysis of data from a HET-CAM(VT) experiment. From a statistical perspective, such data yield many challenges. First of all, the data are typically time-to-event like data, which are at the same time interval censored and right truncated. In addition, one has to cope with overdispersion as well as clustering. Traditional analysis approaches ignore overdispersion and clustering and summarize the data into a continuous score that can be analysed using simple linear models. In this paper, a novel combined frailty model is developed that simultaneously captures all of the aforementioned statistical challenges posed by the data., (Copyright © 2015 John Wiley & Sons, Ltd.)

The recently developed R package INLA (Integrated Nested Laplace Approximation) is becoming a more widely used package for Bayesian inference. The INLA software has been promoted as a fast alternative to MCMC for disease mapping applications. Here, we compare the INLA package to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. We focus on the Poisson data model commonly used for disease mapping. Ultimately, INLA is a computationally efficient way of implementing Bayesian methods and returns nearly identical estimates for fixed parameters in comparison to OpenBUGS, but falls short in recovering the true estimates for the random effects, their precisions, and model goodness of fit measures under the default settings. We assumed default settings for ground truth parameters, and through altering these default settings in our simulation study, we were able to recover estimates comparable to those produced in OpenBUGS under the same assumptions., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Currently, there are no perfect reference tests for the in vivo detection of Neospora caninum infection. Two commercial N caninum ELISA tests are currently used in Belgium for bovine sera (TEST A and TEST B). The goal of this study is to evaluate these tests used at their current cut-offs, with a no gold standard approach, for the test purpose of (1) demonstration of freedom of infection at purchase and (2) diagnosis in aborting cattle. Sera of two study populations, Abortion population (n=196) and Purchase population (n=514), were selected and tested with both ELISA's. Test results were entered in a Bayesian model with informative priors on population prevalences only (Scenario 1). As sensitivity analysis, two more models were used: one with informative priors on test diagnostic accuracy (Scenario 2) and one with all priors uninformative (Scenario 3). The accuracy parameters were estimated from the first model: diagnostic sensitivity (Test A: 93.54 per cent-Test B: 86.99 per cent) and specificity (Test A: 90.22 per cent-Test B: 90.15 per cent) were high and comparable (Bayesian P values >0.05). Based on predictive values in the two study populations, both tests were fit for purpose, despite an expected false negative fraction of ±0.5 per cent in the Purchase population and ±5 per cent in the Abortion population. In addition, a false positive fraction of ±3 per cent in the overall Purchase population and ±4 per cent in the overall Abortion population was found., (British Veterinary Association.)

The basic reproduction number R0 and the effective reproduction number R are pivotal parameters in infectious disease epidemiology, quantifying the transmission potential of an infection in a population. We estimate both parameters from 13 pre-vaccination serological data sets on varicella zoster virus (VZV) in 12 European countries and from population-based social contact surveys under the commonly made assumptions of endemic and demographic equilibrium. The fit to the serology is evaluated using the inferred effective reproduction number R as a model eligibility criterion combined with AIC as a model selection criterion. For only 2 out of 12 countries, the common choice of a constant proportionality factor is sufficient to provide a good fit to the seroprevalence data. For the other countries, an age-specific proportionality factor provides a better fit, assuming physical contacts lasting longer than 15 min are a good proxy for potential varicella transmission events. In all countries, primary infection with VZV most often occurs in early childhood, but there is substantial variation in transmission potential with R0 ranging from 2.8 in England and Wales to 7.6 in The Netherlands. Two non-parametric methods, the maximal information coefficient (MIC) and a random forest approach, are used to explain these differences in R0 in terms of relevant country-specific characteristics. Our results suggest an association with three general factors: inequality in wealth, infant vaccination coverage and child care attendance. This illustrates the need to consider fundamental differences between European countries when formulating and parameterizing infectious disease models., (Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.)

Oxidative stress plays a crucial role in sickle cell disease (SCD) physiopathology. Given that chronic physical activity is known to decrease reactive oxygen species (ROS) and increase nitric oxide (NO) bioavailability in healthy subjects and in patients with cardiovascular or inflammatory pathologies, modulating these factors involved in the severity of the pathology could also be beneficial in SCD. This study aimed to determine if 8 weeks of increased physical activity (PA) by voluntary wheel running affects the hypoxia/reoxygenation (H/R) responses by reducing oxidative stress and increasing NO synthesis in sickle SAD mice. Nitrite/nitrate (NOx) concentrations, NOS3 mRNA expression and phosphorylated-endothelial nitric oxide synthase immunostaining were increased in the lungs of the PA groups after H/R stress. Moreover, lipid peroxidation in the heart was decreased in PA SAD mice. The improvement of antioxidant activity at rest and the decrease in haemolysis may explain this reduced oxidative stress. These results suggest that physical activity probably diminishes some deleterious effects of H/R stress in SAD mice and could be protective against vascular occlusions., (© 2014 John Wiley & Sons Ltd.)

This study investigated whether antimicrobial-resistant Escherichia coli in apparently healthy sows and antimicrobial administration to sows and piglets influenced antimicrobial resistance in fecal commensal E. coli from piglets. Sixty sows from three herds and three of their piglets were sampled at several time points. Antimicrobial usage data during parturition and farrowing were collected. Clinical resistance was determined for two isolates per sampling time point for sows and piglets using disk diffusion. Only 27.4% of E. coli isolates from newborn piglets showed no resistance. Resistance to one or two antimicrobial classes equaled 41.2% and 46.8% in isolates from sows and piglets, respectively, for the overall farrowing period. Multiresistance to at least four classes was found as frequently in sows (15.6%) as in piglets (15.2%). Antimicrobial resistance in piglets was influenced by antimicrobial use in sows and piglets and by the sow resistance level (p≤0.05). Using aminopenicillins and third-generation cephalosporins in piglets affected resistance levels in piglets (odds ratios [OR] >1; p≤0.05). Using enrofloxacin in piglets increased the odds for enrofloxacin resistance in piglets (OR=26.78; p≤0.0001) and sows at weaning (OR=4.04; p≤0.05). For sows, antimicrobial exposure to lincomycin-spectinomycin around parturition increased the resistance to ampicillin, streptomycin, trimethoprim-sulfadiazine in sows (OR=21.33, OR=142.74, OR=18.03; p≤0.05) and additionally to enrofloxacin in piglets (OR=7.50; p≤0.05). This study demonstrates that antimicrobial use in sows and piglets is a risk factor for antimicrobial resistance in the respective animals. Moreover, resistance determinants in E. coli from piglets are selected by using antimicrobials in their dam around parturition.

Low birth weight (LBW) is an important public health issue in the US as well as worldwide. The two main causes of LBW are premature birth and fetal growth restriction. Socio-economic status, as measured by family income has been correlated with LBW incidence at both the individual and population levels. In this paper, we investigate the impact of household income on LBW incidence at different geographical levels. To show this, we choose to examine LBW incidences collected from the state of Georgia, in the US, at both the county and public health (PH) district. The data at the PH district are an aggregation of the data at the county level nested within the PH district. A spatial scaling effect is induced during data aggregation from the county to the PH level. To address the scaling effect issue, we applied a shared multiscale model that jointly models the data at two levels via a shared correlated random effect. To assess the benefit of using the shared multiscale model, we compare it with an independent multiscale model which ignores the scale effect. Applying the shared multiscale model for the Georgia LBW incidence, we have found that income has a negative impact at both the county and PH levels. On the other hand, the independent multiscale model shows that income has a negative impact only at the county level. Hence, if the scale effect is not properly accommodated in the model, a different interpretation of the findings could result.

Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject-specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean-variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero-inflated model. A zero-inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject-specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population-averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero-inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents' involvement in work, to earn money and support their families or themselves, is studied in the second example. Sub-models, which result from omitting zero-inflation and/or overdispersion features, are also considered for comparison's purpose. Analysis of the two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects., (Copyright © 2014 John Wiley & Sons, Ltd.)

Movement of animals from one farm to another is a potential risk and can lead to the spreading of livestock diseases. Therefore, in order to implement effective control measures, it is important to understand the movement network in a given area. Using the SANITEL data from 2005 to 2009, around 2 million cattle movements in Belgium were traced. Exploratory analysis revealed different spatial structures for the movement of different cattle types: fattening calves are mostly moved to the Antwerp region, adult cattle are moved to different parts in Belgium. Based on these differences, movement of cattle would more likely cause a spread of disease to a larger number of areas in Belgium as compared to the fattening calves. A closer inspection of the spatial and temporal patterns of cattle movement using a weighted negative binomial model, revealed a significant short-distance movement of bovine which could be an important factor contributing to the local spreading of a disease. The model however revealed hot spot areas of movement in Belgium; four areas in the Walloon region (Luxembourg, Hainaut, Namur and Liege) were found as hot spot areas while East and West Flanders are important "receivers" of movement. This implies that an introduction of a disease to these Walloon regions could result in a spread toward the East and West Flanders regions, as what happened in the case of Bluetongue BTV-8 outbreak in 2006. The temporal component in the model also revealed a linear trend and short- and long-term seasonality in the cattle movement with a peak around spring and autumn. The result of this explorative analysis enabled the identification of "hot spots" in time and space which is important in enhancing any existing monitoring and surveillance system., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Epidemic data often suffer from underreporting and delay in reporting. In this paper, we investigated the impact of delays and underreporting on estimates of reproduction number. We used a thinned version of the epidemic renewal equation to describe the epidemic process while accounting for the underlying reporting system. Assuming a constant reporting parameter, we used different delay patterns to represent the delay structure in our model. Instead of assuming a fixed delay distribution, we estimated the delay parameters while assuming a smooth function for the reproduction number over time. In order to estimate the parameters, we used a Bayesian semiparametric approach with penalized splines, allowing both flexibility and exact inference provided by MCMC. To show the performance of our method, we performed different simulation studies. We conducted sensitivity analyses to investigate the impact of misspecification of the delay pattern and the impact of assuming nonconstant reporting parameters on the estimates of the reproduction numbers. We showed that, whenever available, additional information about time-dependent underreporting can be taken into account. As an application of our method, we analyzed confirmed daily A(H1N1) v2009 cases made publicly available by the World Health Organization for Mexico and the USA., (Copyright © 2013 John Wiley & Sons, Ltd.)

A new synchrony index for neural activity is defined in this paper. The method is able to measure synchrony dynamics in low firing rate scenarios. It is based on the computation of the time intervals between nearest spikes of two given spike trains. Generalized additive models are proposed for the synchrony profiles obtained by this method. Two hypothesis tests are proposed to assess for differences in the level of synchronization in a real data example. Bootstrap methods are used to calibrate the distribution of the tests. Also, the expected synchrony due to chance is computed analytically and by simulation to assess for actual synchronization.

Very few studies have investigated the effects of exercise on the biological parameters involved in vaso-occlusive events in sickle cell anaemia (SCA). The aim of this study was to test how a mild-moderate endurance exercise modulates oxidative stress, nitric oxide bioavailability and endothelial activation in SCA patients and healthy individuals. Eleven patients with SCA and 15 healthy subjects completed a 20-min duration submaximal cycling exercise at ≈45 Watts. Plasma markers of oxidative stress, antioxidant activity, endothelial activation and nitric oxide bioavailability were investigated before and after the exercise. Nitric oxide levels, anti-oxidant capacity, soluble (s)E-selectin and sP-selectin did not change in response to this exercise. Except for the malondialdehyde levels, which increased in the two groups, the other markers of oxidative stress remained unchanged in both groups in response to exercise. Soluble vascular cell adhesion molecule 1 levels were increased at the end of exercise in both groups. sL-selectin decreased and soluble intercellular adhesion molecule 1 increased with exercise in SCA patients only. The present data suggest that patients with SCA may undertake mild-moderate physical activities without any acute clinical complications, but care should be taken because oxidative stress and endothelial activation significantly increased in some patients., (© 2013 John Wiley & Sons Ltd.)

When Bluetongue Virus Serotype 8 (BTV-8) was first detected in Northern Europe in 2006, several guidelines were immediately put into place with the goal to protect farms and stop the spreading of the disease. This however did not prevent further rapid spread of BTV-8 across Northern Europe. Using information on the 2006 Bluetongue outbreak in cattle farms in Belgium, a spatio-temporal transmission model was formulated. The model quantifies the local transmission of the disease between farms within a municipality, the short-distance transmission between farms across neighbouring municipalities and the transmission as a result of cattle movement. Different municipality-level covariates such as farm density, land composition variables, temperature and precipitation, were assessed as possibly influencing each component of the transmission process. Results showed a significant influence of the different covariates in each model component, particularly the significant effect of temperature and precipitation values in the number of infected farms. The model which allowed us to predict the dynamic spreading of BTV for different movement restriction scenarios, also affirmed the significant impact of cattle movement in the 2006 BTV outbreak pattern. Simulation results further showed the importance of considering the size of restriction zones in the formulation of guidelines for animal infectious diseases.

Background: The aim of this study was to assess the sensitivity of the four major bluetongue surveillance components implemented in Belgium in 2007 for farmed animals and prescribed by the European Union regulation; winter serological screening, sentinel system, passive clinical surveillance, export testing. Scenario tree methodology was used to evaluate the relative sensitivity of detection and targeted approach of each component in terms of early detection and freedom of infection substantiation. Field data collected from the previous year's outbreaks in Belgium were used to determine the risk groups to be considered., Results: The best sensitivities at herd level, taking into account the diagnostic test sensitivity, design prevalence and the number of animals tested within a herd were obtained with the winter screening and sentinel component. The sensitivities at risk group level, taking into account the obtained herd sensitivity, effective probabilities of infection and number of herds tested were high in all components, except for the export component. Component sensitivities ranged between 0.77 and 1 for all components except for the export component with a mean value of 0.22 (0.17-0.26). In terms of early detection, the probability of detection was best using the passive clinical component or the sentinel component. Sensitivity analysis showed that the passive clinical component sensitivity was mostly affected by the diagnostic process and the number of herds sampled. The sentinel and export components sensitivity were mainly affected by the relative risk estimates whereas the winter screening component was mainly affected by the assumptions about the design prevalence., Conclusions: This study revealed interesting features regarding the sensitivity of detection and early detection of infection in the different surveillance components and their risk based approach as requested by the international standards., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Background: Recently, a lot of research has focused on the discovery of novel renal biomarkers. Among others, the urinary kidney injury molecule 1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) have been proven to be promising biomarkers in a wide variety of renal pathologies. However, little is known about the normal concentrations in urine of healthy subjects. Therefore, the goal of our study is to establish reference values for urinary KIM-1, NGAL, N-acetyl-β-D-glucosamidase (NAG), and cystatin C in a healthy population, taking into account possible effects of age and gender., Methods: We collected urine samples from 338 healthy, nonsmoking subjects between 0 and 95 years old. Subjects with elevated α1-microglobulin values were excluded. Next to the urinary concentrations of KIM-1, NGAL, NAG, and cystatin C, we measured urinary creatinine and specific gravity to correct for urinary dilution. The possible effect of age and gender on the four urinary biomarkers was investigated, and the reference values were established., Results: For the absolute urinary concentrations of the biomarkers, age had a significant effect on all the biomarkers, except for cystatin C, whereas gender significantly affected all four of them, except for NAG. The normalization of biomarkers for creatinine and specific gravity had an effect on the correlation between the biomarkers on one hand and age and gender on the other., Conclusions: In conclusion, age and gender had different effects on KIM-1, NGAL, NAG, and cystatin C. Based on this knowledge, age- and gender-specific reference values for KIM-1, NGAL, NAG, and cystatin C were established.

In 2006, Bluetongue serotype 8 was notified for the first time in north-western Europe, more specifically in Belgium, the Netherlands, Luxemburg, Germany and France. The disease spread very rapidly, affecting mainly cattle and sheep farms. In this paper, we examined risk factors affecting the spatial incidence of reported Bluetongue events during the first outbreak in 2006. Previous studies suggested that the Bluetongue incidence was enhanced by environmental factors, such as temperature and wind speed and direction, as well as by human interventions, such as the transport of animals. In contrast to the previous studies, which were based on univariable analyses, a multivariable epidemiological analysis describing the spatial relationship between Bluetongue incidence and possible risk factors is proposed in this paper. This disentangles the complex interplay between different risk factors. Our model shows that wind is the most important factor affecting the incidence of the disease. In addition, areas with high precipitation are slightly more sensitive to the spread of the infection via the wind. Another important risk factor is the land cover; high-risk areas for infection being characterized by a fragmentation of the land cover, especially the combination of forests and urban areas. Precipitation and temperature are also significant risk factors. High precipitation in areas with a large coverage of forests and/or pasture increases the risk whereas high temperature increases the risk considerably in municipalities covered mainly with pasture. Local spread via the vector is strongest in areas with a large coverage of forests and smallest in highly urbanized areas. Finally, the transport of animals from infected areas is a risk factor., (Copyright © 2012 Elsevier B.V. All rights reserved.)