sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se . Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.

sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se. Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways

Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R

Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advances in MCMC methods for these models and their implementation in the R package lgcp. Our suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field. We also present methods for Bayesian inference in two further classes of model based on the log-Gaussian Cox process. The first of these concerns the case where we wish to fit a point process model to data consisting of event-counts aggregated to a set of spatial regions: we demonstrate how this can be achieved using data-augmentation. The second concerns Bayesian inference for a class of marked-point processes specified via a multivariate log-Gaussian Cox process model. For both of these extensions, we give details of their implementation in R

Application of kernel smoothing to estimate the spatio-temporal variation in risk of STEC O157 in England

Identifying geographical areas with significantly higher or lower rates of infectious diseases can provide important aetiological clues to inform the development of public health policy and interventions designed to reduce morbidity. We applied kernel smoothing to estimate the spatial and spatio-temporal variation in risk of STEC O157 infection in England between 2009 and 2015, and to explore differences between the residential locations of cases reporting travel and those not reporting travel. We provide evidence that the distribution of STEC O157 infection in England is non-uniform with respect to the distribution of the at-risk population; that the spatial distribution of the three main genetic lineages infecting humans (I, II and I/II) differs significantly and that the spatio-temporal risk is highly dynamic. Our results also indicate that cases of STEC O157 reporting travel within or outside the UK are more likely to live in the south/south-east of the country, meaning that their residential location may not reflect the location of exposure that led to their infection. We suggest that the observed variation in risk reflects exposure to sources of STEC O157 that are geographically prescribed. These differences may be related to a combination of changes in the strains circulating in the ruminant reservoir, animal movements (livestock, birds or wildlife) or the behavior of individuals prior to infection. Further work to identify the importance of behaviours and exposures reported by cases relative to residential location is needed

Diffusion Smoothing for Spatial Point Patterns

Traditional kernel methods for estimating the spatially-varying density of points in a spatial point pattern may exhibit unrealistic artefacts,in addition to the familiar problems of bias and over or under-smoothing.Performance can be improved by using diffusion smoothing, in which thesmoothing kernel is the heat kernel on the spatial domain. This paper developsdiffusion smoothing into a practical statistical methodology for twodimensionalspatial point pattern data. We clarify the advantages and disadvantagesof diffusion smoothing over Gaussian kernel smoothing. Adaptivesmoothing, where the smoothing bandwidth is spatially-varying, can beperformed by adopting a spatially-varying diffusion rate: this avoids technicalproblems with adaptive Gaussian smoothing and has substantially betterperformance. We introduce a new form of adaptive smoothing using laggedarrival times, which has good performance and improved robustness. Applicationsin archaeology and epidemiology are demonstrated. The methods areimplemented in open-source R cod

The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales

Improving water resources management using different irrigation strategies and water qualities: Field and modelling study

The aim of this study was to investigate the effects of two different irrigation strategies, regulated deficit irrigation, RDI and partial root drying, PRD using surface freshwater (SW) and brackish treated wastewater (TWW) for maize and potato crops. The SALTMED model has been applied using the field measurements of two cropping seasons 2013 and 2014 at the Canale Emiliano Romagnolo, CER’s experimental farm located in Mezzolara di Budrio (Bologna, Italy). In 2013, PRD irrigated potato received 17% less irrigation water than RDI but produced nearly the same yield as under RDI. The water productivity, o naverage, was 11% higher for PRD compared with RDI. For maize 2014 season, the PRD strategy received almost 15% less irrigation water, but produced a yield only 6% lower than that of RDI and gave equal water productivity to RDI. Given that the two strategies received the same amount of rainfall the results favour the PRD over RDI. Had the site not received above average rainfall (258 mm in 2013 and 259 mm during the 2014 growing seasons), PRD might have produced higher yield and water productivity than RDI. In terms of model simulations, overall, the model showed a strong relationship between the observed and the simulated soil moisture and salinity profiles, total dry mater and final yields. This illustrates SALTMED model’s ability to simulate the dry matter and yield of C3 and C4 crops as well as to simulated different water qualities and different water application strategies. Therefore, the model can run with “what if” scenarios depicting several water qualities, crops and irrigation systems and strategies without the need to try them all in the field. This will reduce costs of labour and investment

Clinical relevance of biomarkers of oxidative stress

SIGNIFICANCE Oxidative stress is considered to be an important component of various diseases. A vast number of methods have been developed and used in virtually all diseases to measure the extent and nature of oxidative stress, ranging from oxidation of DNA to proteins, lipids, and free amino acids. Recent Advances: An increased understanding of the biology behind diseases and redox biology has led to more specific and sensitive tools to measure oxidative stress markers, which are very diverse and sometimes very low in abundance. CRITICAL ISSUES The literature is very heterogeneous. It is often difficult to draw general conclusions on the significance of oxidative stress biomarkers, as only in a limited proportion of diseases have a range of different biomarkers been used, and different biomarkers have been used to study different diseases. In addition, biomarkers are often measured using nonspecific methods, while specific methodologies are often too sophisticated or laborious for routine clinical use. FUTURE DIRECTIONS Several markers of oxidative stress still represent a viable biomarker opportunity for clinical use. However, positive findings with currently used biomarkers still need to be validated in larger sample sizes and compared with current clinical standards to establish them as clinical diagnostics. It is important to realize that oxidative stress is a nuanced phenomenon that is difficult to characterize, and one biomarker is not necessarily better than others. The vast diversity in oxidative stress between diseases and conditions has to be taken into account when selecting the most appropriate biomarker. Antioxid. Redox Signal. 00, 000-000