Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort.

Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

Bayesian spatio-temporal analysis of malaria prevalence in children between 2 and 10 years of age in Gabon.

BACKGROUND: Gabon still bears significant malaria burden despite numerous efforts. To reduce this burden, policy-makers need strategies to design effective interventions. Besides, malaria distribution is well known to be related to the meteorological conditions. In Gabon, there is limited knowledge of the spatio-temporal effect or the environmental factors on this distribution. This study aimed to investigate on the spatio-temporal effects and environmental factors on the distribution of malaria prevalence among children 2-10 years of age in Gabon. METHODS: The study used cross-sectional data from the Demographic Health Survey (DHS) carried out in 2000, 2005, 2010, and 2015. The malaria prevalence was obtained by considering the weighting scheme and using the space-time smoothing model. Spatial autocorrelation was inferred using the Moran's I index, and hotspots were identified with the local statistic Getis-Ord General Gi. For the effect of covariates on the prevalence, several spatial methods implemented in the Integrated Nested Laplace Approximation (INLA) approach using Stochastic Partial Differential Equations (SPDE) were compared. RESULTS: The study considered 336 clusters, with 153 (46%) in rural and 183 (54%) in urban areas. The prevalence was highest in the Estuaire province in 2000, reaching 46%. It decreased until 2010, exhibiting strong spatial correlation (P < 0.001), decreasing slowly with distance. Hotspots were identified in north-western and western Gabon. Using the Spatial Durbin Error Model (SDEM), the relationship between the prevalence and insecticide-treated bed nets (ITNs) coverage was decreasing after 20% of coverage. The prevalence in a cluster decreased significantly with the increase per percentage of ITNs coverage in the nearby clusters, and per degree Celsius of day land surface temperature in the same cluster. It slightly increased with the number of wet days and mean temperature per month in neighbouring clusters. CONCLUSIONS: In summary, this study showed evidence of strong spatial effect influencing malaria prevalence in household clusters. Increasing ITN coverage by 20% and prioritizing hotspots are essential policy recommendations. The effects of environmental factors should be considered, and collaboration with the national meteorological department (DGM) for early warning systems is needed.

Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation.

Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.

Modeling spatial pattern of anemia and malnutrition co-occurrence among under-five children in Ethiopia: A Bayesian geostatistical approach.

Anemia and malnutrition among under-five children are some of the challenges to public health in Ethiopia. This study aims to determine the socio-economic, demographic, and geographical risk factors that increase the prevalence of the co-occurrence of anemia and malnutrition among under-five children in Ethiopia. The Ethiopia Demographic and Health Survey data for the survey years 2011 and 2016 were used. A Bayesian hierarchical mixed model with a stochastic partial differential equation was adopted to understand the spatial patterns of co-occurrence of these ailments in Ethiopia. The significant risk factors are gender, maternal education, birth order, preceding births, contraceptive use, vaccination, marital status, distance to a health facility, and birth weight. Findings revealed more vulnerability among children less than twenty months and existing geographical disparity with a higher burden of the prevalence of the co-occurrences of anemia and malnutrition in the North-East regions. For cost-effective intervention, policies and programs that improve individual-level risk factors of parents are a more promising approach to tackle these ailments in high-prevalent regions than the ones on the children and should be of utmost priority in the North-East region of the country.

Bayesian and network models with covariate effects for predicting heating energy demand.

The spatial effect is an element presented in many geostatistical works and it should be incorporated into studies regarding the heating energy demand of residential building stocks. The most common approaches have been made by simple descriptive statistics or using analyses by Markov random fields. In this work, we propose two different methods. First, the Stochastic Partial Differential Equation with the Integrated Nested Laplace Approximation to model the variable heating energy demand in Castellón de la Plana, Spain also considering covariates and the spatial effect. Second, simulated street networks for analysing data. We describe and take advantage of the Bayesian methodology in the modelling process in all the scenarios, including covariates and the possibility of creating a simulated street network with the data for the modelling issue. Our results show that the spatial location of the building is a crucial element to study the heating energy demand using both methodologies.

Mapping ex ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data.

A rapid response to global infectious disease outbreaks is crucial to protect public health. Ex ante information on the spatial probability distribution of early infections can guide governments to better target protection efforts. We propose a two-stage statistical approach to spatially map the ex ante importation risk of COVID-19 and its uncertainty across Indonesia based on a minimal set of routinely available input data related to the Indonesian flight network, traffic and population data, and geographical information. In a first step, we use a generalised additive model to predict the ex ante COVID-19 risk for 78 domestic Indonesian airports based on data from a global model on the disease spread and covariates associated with Indonesian airport network flight data prior to the global COVID-19 outbreak. In a second step, we apply a Bayesian geostatistical model to propagate the estimated COVID-19 risk from the airports to all of Indonesia using freely available spatial covariates including traffic density, population and two spatial distance metrics. The results of our analysis are illustrated using exceedance probability surface maps, which provide policy-relevant information accounting for the uncertainty of the estimates on the location of areas at risk and those that might require further data collection.

A spatiotemporal analysis of NO 2 concentrations during the Italian 2020 COVID-19 lockdown.

When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space and time-the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO 2 ) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around - 25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures. There are two aspects of our research that are equally interesting. First, we provide a unique statistical perspective for calculating the relative change in the NO 2 by jointly modeling pollutant concentrations time series. Second, as an output we provide a collection of weekly continuous maps, describing the spatial pattern of the NO 2 2019/2020 relative changes.

A practical approach for geographic prioritization and targeting of insecticide-treated net distribution campaigns during public health emergencies and in resource-limited settings.

BACKGROUND: The use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources. METHODS: A GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions. RESULTS: A case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as 'extremely high' or 'high' priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions. CONCLUSION: The authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.

Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique.

Bayesian spatial models are widely used to analyse data that arise in scientific disciplines such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo (MCMC) methods have been used to fit these type of models. However, these are highly computationally intensive methods that present a wide range of issues in terms of convergence and can become infeasible in big data problems. The integrated nested Laplace approximation (INLA) method is a computational less-intensive alternative to MCMC that allows us to perform approximate Bayesian inference in latent Gaussian models such as generalised linear mixed models and spatial and spatio-temporal models. This approach can be used in combination with the stochastic partial differential equation (SPDE) approach to analyse geostatistical data that have been collected at particular sites to predict the spatial process underlying the data as well as to assess the effect of covariates and model other sources of variability. Here we demonstrate how to fit a Bayesian spatial model using the INLA and SPDE approaches applied to freely available data of malaria prevalence and risk factors in Mozambique. We show how to fit and interpret the model to predict malaria risk and assess the effect of covariates using the R-INLA package, and provide the R code necessary to reproduce the results or to use it in other spatial applications.

Variation in use of Caesarean section in Norway: An application of spatio-temporal Gaussian random fields.

AIMS: Caesarean section (CS) is a medical intervention performed in Norway when a surgical delivery is considered more beneficial than a vaginal. Because deliveries with higher risk are centralized to larger hospitals, use of CS varies considerably between hospitals. We describe how the use of CS varies geographically by municipality. Since indications for CS should have little variation across the relatively homogenous population of Norway, we expect fair use of CS to be evenly distributed across the municipalities. METHODS: Data from the Medical Birth Registry of Norway were used in our analyses (810,914 total deliveries, 133,746 CSs, 440 municipalities). We propose a spatial correlation model that takes the location into account to describe the variation in use of CS across the municipalities. The R packages R-INLA and TMB are used to estimate the yearly municipal CS rate and the spatial correlation between the municipalities. We also apply stratified models for different categories of delivering women (Robson groups). Estimated rates are displayed in maps and model parameters are shown in tables. RESULTS: The CS rate varies substantially between the different municipalities. As expected, there was strong correlation between neighbouring municipalities. Similar results were found for different Robson groups. CONCLUSIONS: The substantial difference in CS use across municipalities in Norway is not likely to be due to specific medical reasons, but rather to hospitals' different policies towards the use of CS. The policy to be either more or less restrictive to CS was not specific to any category of deliveries.

A white noise approach to evolutionary ecology.

Although the evolutionary response to random genetic drift is classically modelled as a sampling process for populations with fixed abundance, the abundances of populations in the wild fluctuate over time. Furthermore, since wild populations exhibit demographic stochasticity and since random genetic drift is in part due to demographic stochasticity, theoretical approaches are needed to understand the role of demographic stochasticity in eco-evolutionary dynamics. Here we close this gap for quantitative characters evolving in continuously reproducing populations by providing a framework to track the stochastic dynamics of abundance density across phenotypic space using stochastic partial differential equations. In the process we develop a set of heuristics to operationalize the powerful, but abstract theory of white noise and diffusion-limits of individual-based models. Applying these heuristics, we obtain stochastic ordinary differential equations that generalize classical expressions of ecological quantitative genetics. In particular, by supplying growth rate and reproductive variance as functions of abundance densities and trait values, these equations track population size, mean trait and additive genetic variance responding to mutation, demographic stochasticity, random genetic drift, deterministic selection and noise-induced selection. We demonstrate the utility of our approach by formulating a model of diffuse coevolution mediated by exploitative competition for a continuum of resources. In addition to trait and abundance distributions, this model predicts interaction networks defined by niche-overlap, competition coefficients, or selection gradients. Using a high-richness approximation, we find linear selection gradients and competition coefficients are uncorrelated, but magnitudes of linear selection gradients and quadratic selection gradients are both positively correlated with competition coefficients. Hence, competing species that strongly affect each other's abundance tend to also impose selection on one another, but the directionality is not predicted. This approach contributes to the development of a synthetic theory of evolutionary ecology by formalizing first principle derivations of stochastic models tracking feedbacks of biological processes and the patterns of diversity they produce.

District-level estimation of vaccination coverage: Discrete vs continuous spatial models.

Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low- and middle-income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model-based approaches for producing subnational estimates of HDIs using survey data, particularly cluster-level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district-level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district-level data with continuous Gaussian process (GP) models that utilize geolocated cluster-level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015-16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between-cluster variation in the continuous GP models did not have any real effect on the district-level estimates. Our results provide guidance to practitioners on the reliability of these model-based approaches for producing estimates of vaccination coverage and other HDIs.

Approximate Bayesian inference for multivariate point pattern analysis in disease mapping.

We present a novel approach for analysing multivariate case-control georeferenced data in a Bayesian disease mapping context using stochastic partial differential equations (SPDEs) and the integrated nested Laplace approximation (INLA) for model fitting. In particular, we propose smooth terms based on SPDE models to estimate the underlying spatial variation as well as risk associated to pollution sources. Log-Gaussian Cox processes are used to estimate the intensity of the cases and controls, to account for risk factors and include a term to measure spatial residual variation. Each intensity is modelled on a baseline spatial effect (estimated from both controls and cases), a disease-specific spatial term and the effects of some covariates. By fitting these models, the residual spatial terms can be easily compared to detect high-risk areas not explained by the covariates. Three different types of effects to model exposure to pollution sources are considered on the distance to the source: a fixed effect, a smooth term to model non-linear effects by means of a discrete random walk of order one and a Gaussian process in one dimension with a Matérn covariance function. Spatial terms are modelled using a Gaussian process in two dimensions with a Matérn covariance function and are approximated using an approach based on solving an SPDE through INLA. Finally, this new framework is applied to a dataset of three different types of cancer and a set of controls from Alcalá de Henares (Madrid, Spain). Covariates available include the distance to several polluting industries and socioeconomic indicators. Our findings point to a possible risk increase due to the proximity to some of these industries.

Application of spatial analysis to investigate contribution of VOCs to photochemical ozone creation.

This study was concerned with the temporal analysis of benzene, toluene, ethylbenzene, xylenes (BTEXs), and ozone in Rochester, New York, between 2012 and 2018. Additionally, the influence of ozone precursors (e.g., BTEXs and NO2) and meteorological variables (e.g., relative humidity (RH), temperature along with wind speed) on ozone dispersion was investigated in the eastern half of the USA using the integrated nested Laplace approximation and stochastic partial differential equation (INLA-SPDE). The benzene variability at seasonal scale was characterized by higher values during the cold seasons. On the contrary, the long-term temporal trend of ozone depicted a repetitive cyclic behavior while an episode, with values exceeding 5 µg/m3, was detected associated with benzene in 2015. The spatial analysis by INLA-SPDE indicated that 1,3,5-trimethylbenzene and benzene were the key ozone precursors influencing ozone formation. It was demonstrated that increase of temperature had a considerable impact on ozone build-up whereas the increment of RH leads to decrease in ambient values of ozone. The amounts of root mean squared error (RMSE), mean absolute error (MAE), and bias for the validation data (e.g., 32 samples) were 0.005, 0.004, and 0.0008, exhibiting a reasonable out-of-sample forecasting by the INLA-SPDE model. The distribution map of ozone highlighted a hot spot in the state of Florida.

Spatio-temporal hierarchical Bayesian analysis of wildfires with Stochastic Partial Differential Equations. A case study from Valencian Community (Spain).

The spatio-temporal study of wildfires has two complex elements that are the computational efficiency and longtime processing. Modelling the spatial variability of a wildfire could be performed in different ways, and an important issue is the computational facilities that the new methodological techniques afford us. The Markov random fields methods have made possible to build risk maps, but for many forest managers, it is more advantageous to know the size of the fire and its location. In the first part of this work, Stochastic Partial Differential Equation with Integrated Nested Laplace Approximation is utilised to model the size of the forest fires observed in the Valencian Community (Spain) and so it does the inclusion of the time effect, and the study of the emergency calls. The most crucial element in this paper is the inclusion of the improved meshes for the spatial effect and the time, these are, 2d (locations) and 1d (time) respectively. The advantage of the use of spatio-temporal meshes is described with the inclusion of Bayesian methodology in all the scenarios.

A spatial-temporal study of dengue in Peninsular Malaysia for the year 2017 in two different space-time model.

Spatio-temporal disease mapping models give a great worth in epidemiology, especially in describing the pattern of disease incidence across geographical space and time. This paper analyses the spatial and temporal variability of dengue disease rates based on generalized linear mixed models. For spatio-temporal study, the models incorporate spatially correlated random effects as well as temporal effects. In this study, two different spatial random effects are applied and compared. The first model is based on Leroux spatial model, while the second model is based on the stochastic partial differential equation approach. For the temporal effects, both models follow an autoregressive model of first-order model. The models are fitted within a hierarchical Bayesian framework with integrated nested Laplace approximation methodology. The main objective of this study is to compare both spatio-temporal models in terms of their ability in representing the disease phenomenon. The models are applied to weekly dengue fever data in Peninsular Malaysia reported to the Ministry of Health Malaysia in the year 2017 according to the district level.

A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping.

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Needle-based extraction techniques with protected sorbent as powerful sample preparation tools to gas chromatographic analysis: Trends in application.

Microextraction techniques are widely applied for sample preparation to gas chromatographic analysis of target compounds in samples with a complex matrix. Recently, needle-based microextraction techniques have been developed in order to improve performance of the extraction. The main advantages of these techniques are miniaturization, automation, high performance, environmentally friendliness and on-line coupling with analytical instruments. This review focuses on the three needle-based microextraction techniques including solid-phase dynamic extraction (SPDE), in-tube extraction (ITEX) and needle trap device extraction (NTD). The core of the aforementioned techniques is an extraction phase protected in the stainless steel needle. The application of the sorbent-protected needle extraction techniques for the gas chromatographic analysis of environmental, biological and food samples is discussed. The fundamental aspects and development over the years are also summarized.

Urinary excretion of heptanones, heptanoles and 2,5-heptanedione after controlled acute exposure of volunteers to n-heptane.

A lack of well-established parameters and assessment values currently impairs biomonitoring of n-heptane exposure. Using controlled inhalation experiments, we collected information on urinary n-heptane metabolite concentrations and the time course of metabolite excretion. Relationships between external and internal exposure were analysed to investigate the suitability of selected metabolites to reflect n-heptane uptake. Twenty healthy, non-smoking males (aged 19-38 years, median 25.5) were exposed for 3 h to 167, 333 and 500 ppm n-heptane, each. Spot urine samples of the volunteers, collected before exposure and during the following 24 h, were analysed for heptane-2-one, 3-one, 4-one, 2,5-dione, 1-ol, 2-ol, 3-ol, and 4-ol using headspace solid phase dynamic extraction gas chromatography/mass spectrometry (HS-SPDE-GC/MS). Starting from median pre-exposure concentrations between <0.5 (3-one) and 82.9 µg/L (4-one), exposure increased the concentrations for all parameters except for 4-one. Median post-exposure concentrations ranged up to 840.4 µg/L (2-ol) and decreased with half-lifes <3 h after exposure. Non-parametric correlation analyses (n = 47, p < 0.05) revealed weak to moderate associations of volume related metabolite excretion with external exposure for 2-one, 3-one and 2,5-dione (R = 0.332-0.753). Heptanol excretion was moderately associated with exposure (R ≥ 0.509) only after creatinine adjustment. Lacking association with external exposure impedes the use of 4-one as heptane biomarker, whereas 2-ol and 3-ol turned out to be sensitive indicators of exposure if creatinine correction is applied. By providing fundamental data on a panel of eight potential heptane metabolites, our study can help to promote biological monitoring of n-heptane exposure.

Using additive and coupled spatiotemporal SPDE models: a flexible illustration for predicting occurrence of Culicoides species.

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.