Fedele Greco

Spatial reconstruction of rainfall fields from rain gauge and radar data

Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.

A multivariate CAR model for improving the estimation of relative risks

Disease mapping studies have been widely performed at univariate level, that is considering only one disease in the estimated models. Nonetheless, simultaneous modelling of different diseases can be a valuable tool both from the epidemiological and from the statistical point of view. In this paper we propose a model for multivariate disease mapping that generalizes the univariate conditional auto-regressive distribution. The proposed model is proven to be an effective alternative to existing multivariate models, mainly because it overcome some restrictive hypotheses underlying models previously proposed in this context. Model performances are checked via a simulation study and via application to a case study.

Some Interpolation Estimators in Environmental Risk Assessment for Spatially Misaligned Health Data

Ecological regression studies are widely used in geographical epidemiology to assess the relationships between health hazard and putative risk factors. Very often, health data are measured at an aggregate level because of confidentiality restrictions, while putative risk factors are measured on a different grid, i.e., independent (exposure) variable and response (counts) variable are spatially misaligned. To perform a regression of risk on exposure, one needs to realign the spatial support of the variables. Bayesian hierarchical models constitute a natural approach to the problem because of their ability to model the exposure field and the relationship between exposure and relative risk at different levels of the hierarchy, taking proper account of the variability induced by the covariate estimation. In the current paper, we propose two fully Bayesian solutions to the problem. The first one is based on the kernel-smoothing technique, while the second one is built on the tessellation of the study region. We illustrate our methods by assessing the relationship between exposure to uranium in drinkable waters and cancer incidence, in South Carolina (USA).

Spatio-temporal regression on compositional covariates: modeling vegetation in a gypsum outcrop

Investigating the relationship between vegetation cover and substrate typologies is important for habitat conservation. To study these relationships, common practice in modern ecological surveys is to collect information regarding vegetation cover and substrate typology over fine regular lattices, as derived from digital ground photos. Information on substrate typologies is often available as compositional measures, e.g., the area proportion occupied by a certain substrate. Two primary issues are of interest for ecologists: first, how much substrate typologies differ in terms of relative suitability for vegetation cover and, second, whether suitability varies over time. This paper develops a procedure for managing compositional covariates within a Bayesian hierarchical framework to effectively address the aforementioned issues. A spatio-temporal model is adopted to estimate the temporal pattern characterizing substrate relative suitability for vegetation cover and, at the same time, to account for spatio-temporal correlation. Relative suitability is modeled by time-varying regression coefficients, and spatial, temporal and spatio-temporal random effects are modeled using Gaussian Markov Random Field models.

Non-parametric regression on compositional covariates using Bayesian P-splines

Methods to perform regression on compositional covariates have recently been proposed using isometric log-ratios (ilr) representation of compositional parts. This approach consists of first applying standard regression on ilr coordinates and second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts. This gives easy-to-interpret parameters indicating the relative effect of each compositional part. In this work we present an extension of this framework, where compositional covariate effects are allowed to be smooth in the ilr domain. This is achieved by fitting a smooth function over the multidimensional ilr space, using Bayesian P-splines. Smoothness is achieved by assuming random walk priors on spline coefficients in a hierarchical Bayesian framework. The proposed methodology is applied to spatial data from an ecological survey on a gypsum outcrop located in the Emilia Romagna Region, Italy.

The Comparative Efficacy and Tolerability of Diclofenac 0.1% and Bromfenac 0.09% Ophthalmic Solutions after Cataract Surgery

ABSTRACT Purpose: To compare the efficacy and tolerability of diclofenac and bromfenac ophthalmic solutions as adjunctive therapy after cataract surgery. Materials and Methods: This prospective randomized controlled study included 130 patients who underwent cataract surgery. One hundred patients were randomized to receive postoperatively diclofenac 0.1% (four times daily for 28 days, Group 1) or bromfenac 0.09% (twice daily for 14 days, Group 2) ophthalmic solutions in addition to steroid-antibiotic combination. Thirty patients instilled only steroid-antibiotic combination (Control Group 3). Laser flare-cell photometry and optical coherence tomography scans with central foveal thickness (CFT) measurement were performed before (V0) and 7 (V1), 14 (V2) and 28 days (V3) after surgery. Treatment tolerability was scored using the Ocular Comfort Grading Assessment. Results: Laser flare-cell photometry values were significantly higher at V1, V2 and V3 compared to V0 in all the groups (respectively 13.3 ± 1.0, 12.7 ± 0.9 and 9.6 ± 0.9 vs 8.4 ± 0.6 ph/ms for Group 1; 13.4 ± 1.0, 12.7 ± 0.9 and 12.7 ± 1.0 vs 8.1 ± 0.6 for Group 2; 15.9 ± 0.8, 15.4 ± 0.7 and 14.5 ± 0.7 vs 7.5 ± 0.5 for Group 3) (p < 0.001); flare increase was significantly lower in Group 1 compared to Groups 2 and 3 (p < 0.001). CFT values were higher after surgery in all the three groups; the increase was significantly lower in Group 1 compared to Groups 2 and 3 (p < 0.0002). The percentage of symptoms-free patients after study treatment was significantly higher in Group 2 compared to Group 1 (respectively 74% vs 14% of the total; p < 0.001). Conclusion: The addition of diclofenac or bromfenac ophthalmic solutions contributed to further reduce both inflammation and cystoid macular edema after cataract surgery compared to steroid-antibiotic combination alone. Diclofenac appeared to be more effective in reducing postoperative intraocular inflammation with a more intense and prolonged regimen, while bromfenac more tolerated with lower daily dose and treatment duration.

In vivo and in vitro effects of selected antioxidants on rabbit meat microbiota

The purpose of this study was to investigate the effect of dietary vitamin E or EconomasE™ supplementation on the growth of several background/pathogenic bacteria on rabbit carcasses and hamburgers during refrigerated storage. For 51days, 270 New Zealand rabbits received either a basal diet, or experimental diets enriched with 100 or 200mg/kg of vitamin E or EconomasE™. The bacteria studied were Salmonella, Listeria monocytogenes, Pseudomonas, Enterobacteriaceae, Escherichia coli, coagulase-positive staphylococci, plus both mesophilic and psychrotrophic aerobes. The growth of Listeria monocytogenes on contaminated patties was evaluated through a challenge test. The potential protective or antimicrobial effect of vitamin E or EconomasE™ on Listeria monocytogenes or Pseudomonas aeruginosa was assessed in vitro. Diet did not influence the concentrations of bacteria found on rabbit carcasses and developing on hamburgers. Vitamin E (in vivo and in vitro) and EconomasE™ in vivo had a protective antioxidant role, while EconomasE™ in vitro had strong antibacterial activity against Listeria monocytogenes, but not against Pseudomonas aeruginosa.

Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

ABSTRACT This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.