Spatial algorithm for detecting disease outbreaks in Australia

La detección temprana de brotes de enfermedades es esencial de cara a una intervención pronta en problemas de salud pública. Actualmente en Australia, las enfermedades notificables son recogidas y almacenadas, y referenciadas geográfica y temporalmente. Sin embargo, el proceso para la búsqueda de brotes de enfermedad sobre escalas espaciales distintas no está bien definido. Los brotes son de detección difícil. Algunas enfermedades aparecen relativamente rápido, mientras otras requieren más tiempo para su incubación y sólo se hacen evidentes sobre largos intervalos temporales. En la práctica, los epidemiólogos combinan diferentes conjuntos de evidencias para determinar la probabilidad de la existencia de un brote. Gracias al progresivo incremento de disponibilidad de bases de datos electrónicas y de los Sistemas de Información Geográfica (SIG), el potencial para la utilización de técnicas de análisis espacial para la visualización, exploración y modelado de notificaciones de enfermedades para la detección temprana de brotes, es hoy mayor que en el pasado. En este artículo, los autores presentan un algoritmo que emplea bases de datos de la administración, análisis espacial y SIG para la detección de clusters de enfermedades en el Estado de Australia Occidental. El algoritmo revisa los códigos postales de forma rutinaria hasta encontrar un número de casos que supera los valores que serían esperados en la región considerada. El algoritmo está diseñado para su uso por profesionales de la salud pública para asistir en la identificación y seguimiento de clusters en tiempo real.The early detection of disease outbreaks is essential for early intervention in potential public health problems. Currently in Australia, disease notifications are recorded, temporally and geographically referenced; however, the process of searching for outbreaks over different spatial scales is not well defined. Disease outbreaks are difficult to detect. Some diseases appear relatively rapidly, while others take time to gestate and become apparent over long time intervals. In practice, epidemiologists combine different sets of evidence in different ways and apply reasoning to determine the likelihood of an outbreak. With an increase in the availability of electronic health-care data and geographic information systems (GIS), there is great potential to use spatial analysis techniques for the visualisation, exploration and modelling of disease notifications for the early detection of disease outbreaks. In this paper, the authors present an algorithm that uses administrative databases, spatial analysis and GIS for the detection of disease clusters in Western Australia (WA). The algorithm routinely tests administrative areas (postcodes) and highlights the areas in which counts exceed the expected number for the particular region. This algorithm is intended to be used by public health officials to identify and track clusters in localised geographic areas in real-time

Spatial algorithm for detecting disease outbreaks in Australia

La detección temprana de brotes de enfermedades es esencial de cara a una intervención pronta en problemas de salud pública. Actualmente en Australia, las enfermedades notificables son recogidas y almacenadas, y referenciadas geográfica y temporalmente. Sin embargo, el proceso para la búsqueda de brotes de enfermedad sobre escalas espaciales distintas no está bien definido. Los brotes son de detección difícil. Algunas enfermedades aparecen relativamente rápido, mientras otras requieren más tiempo para su incubación y sólo se hacen evidentes sobre largos intervalos temporales. En la práctica, los epidemiólogos combinan diferentes conjuntos de evidencias para determinar la probabilidad de la existencia de un brote. Gracias al progresivo incremento de disponibilidad de bases de datos electrónicas y de los Sistemas de Información Geográfica (SIG), el potencial para la utilización de técnicas de análisis espacial para la visualización, exploración y modelado de notificaciones de enfermedades para la detección temprana de brotes, es hoy mayor que en el pasado. En este artículo, los autores presentan un algoritmo que emplea bases de datos de la administración, análisis espacial y SIG para la detección de clusters de enfermedades en el Estado de Australia Occidental. El algoritmo revisa los códigos postales de forma rutinaria hasta encontrar un número de casos que supera los valores que serían esperados en la región considerada. El algoritmo está diseñado para su uso por profesionales de la salud pública para asistir en la identificación y seguimiento de clusters en tiempo real.The early detection of disease outbreaks is essential for early intervention in potential public health problems. Currently in Australia, disease notifications are recorded, temporally and geographically referenced; however, the process of searching for outbreaks over different spatial scales is not well defined. Disease outbreaks are difficult to detect. Some diseases appear relatively rapidly, while others take time to gestate and become apparent over long time intervals. In practice, epidemiologists combine different sets of evidence in different ways and apply reasoning to determine the likelihood of an outbreak. With an increase in the availability of electronic health-care data and geographic information systems (GIS), there is great potential to use spatial analysis techniques for the visualisation, exploration and modelling of disease notifications for the early detection of disease outbreaks. In this paper, the authors present an algorithm that uses administrative databases, spatial analysis and GIS for the detection of disease clusters in Western Australia (WA). The algorithm routinely tests administrative areas (postcodes) and highlights the areas in which counts exceed the expected number for the particular region. This algorithm is intended to be used by public health officials to identify and track clusters in localised geographic areas in real-time

Developing Intensity-Duration-Frequency (IDF) Curves From Satellite-Based Precipitation: Methodology and Evaluation

Given the continuous advancement in the retrieval of precipitation from satellites, it is important to develop methods that incorporate satellite-based precipitation data sets in the design and planning of infrastructure. This is because in many regions around the world, in situ rainfall observations are sparse and have insufficient record length. A handful of studies examined the use of satellite-based precipitation to develop intensity-duration-frequency (IDF) curves; however, they have mostly focused on small spatial domains and relied on combining satellite-based with ground-based precipitation data sets. In this study, we explore this issue by providing a methodological framework with the potential to be applied in ungauged regions. This framework is based on accounting for the characteristics of satellite-based precipitation products, namely, adjustment of bias and transformation of areal to point rainfall. The latter method is based on previous studies on the reverse transformation (point to areal) commonly used to obtain catchment-scale IDF curves. The paper proceeds by applying this framework to develop IDF curves over the contiguous United States (CONUS); the data set used is Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR). IDFs are then evaluated against National Oceanic and Atmospheric Administration (NOAA) Atlas 14 to provide a quantitative estimate of their accuracy. Results show that median errors are in the range of (17–22%), (6–12%), and (3–8%) for one-day, two-day and three-day IDFs, respectively, and return periods in the range (2–100) years. Furthermore, a considerable percentage of satellite-based IDFs lie within the confidence interval of NOAA Atlas 14

Semi-automatic segmentation of the fetal brain from magnetic resonance imaging

Background: Volumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in magnetic resonance images (MRI) are often manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compared the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings. Methods: Adult women with singleton pregnancies (n = 21), of whom five were scanned twice, approximately 3 weeks apart, were recruited [26 total datasets, median gestational age (GA) = 34.8, IQR = 30.9–36.6]. T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB’s Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSCs). The DSC values were compared using Friedman’s test for repeated measures. Results: Comparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.72 [interquartile range (IQR) = 0.6–0.8] and 0.54 (IQR = 0.4–0.6), respectively. A Friedman’s test indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT (p \u3c 0.001). Conclusion: Deformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development

Glucagon-Like Peptide 1 Receptor Activation Augments Cardiac Output and Improves Cardiac Efficiency in Obese Swine After Myocardial Infarction

This study tested the hypothesis that glucagon-like peptide 1 (GLP-1) therapies improve cardiac contractile function at rest and in response to adrenergic stimulation in obese swine after myocardial infarction. Obese Ossabaw swine were subjected to gradually developing regional coronary occlusion using an ameroid occluder placed around the left anterior descending coronary artery. Animals received subcutaneous injections of saline or liraglutide (0.005-0.015 mg/kg/day) for 30 days after ameroid placement. Cardiac performance was assessed at rest and in response to sympathomimetic challenge (dobutamine 0.3-10 μg/kg/min) using a left ventricular pressure/volume catheter. Liraglutide increased diastolic relaxation (dP/dt; Tau 1/2; Tau 1/e) during dobutamine stimulation (P < 0.01) despite having no influence on the magnitude of myocardial infarction. The slope of the end-systolic pressure volume relationship (i.e., contractility) increased with dobutamine after liraglutide (P < 0.001) but not saline administration (P = 0.63). Liraglutide enhanced the slope of the relationship between cardiac power and pressure volume area (i.e., cardiac efficiency) with dobutamine (P = 0.017). Hearts from animals treated with liraglutide demonstrated decreased β1-adrenoreceptor expression. These data support that GLP-1 agonism augments cardiac efficiency via attenuation of maladaptive sympathetic signaling in the setting of obesity and myocardial infarction

Limit theorems for von Mises statistics of a measure preserving transformation

For a measure preserving transformation $T$ of a probability space $(X,\mathcal F,\mu)$ we investigate almost sure and distributional convergence of random variables of the form $$x \to \frac{1}{C_n} \sum_{i_1<n,...,i_d<n} f(T^{i_1}x,...,T^{i_d}x),\, n=1,2,...,$$ where $f$ (called the \emph{kernel}) is a function from $X^d$ to $\R$ and $C_1, C_2,...$ are appropriate normalizing constants. We observe that the above random variables are well defined and belong to $L_r(\mu)$ provided that the kernel is chosen from the projective tensor product $$L_p(X_1,\mathcal F_1, \mu_1) \otimes_{\pi}...\otimes_{\pi} L_p(X_d,\mathcal F_d, \mu_d)\subset L_p(\mu^d)$$ with $p=d\,r,\, r\ \in [1, \infty).$ We establish a form of the individual ergodic theorem for such sequences. Next, we give a martingale approximation argument to derive a central limit theorem in the non-degenerate case (in the sense of the classical Hoeffding's decomposition). Furthermore, for $d=2$ and a wide class of canonical kernels $f$ we also show that the convergence holds in distribution towards a quadratic form $\sum_{m=1}^{\infty} \lambda_m\eta^2_m$ in independent standard Gaussian variables $\eta_1, \eta_2,...$. Our results on the distributional convergence use a $T$--\,invariant filtration as a prerequisite and are derived from uni- and multivariate martingale approximations

Testing fluvial erosion models using the transient response of bedrock rivers to tectonic forcing in the Apennines, Italy

The transient response of bedrock rivers to a drop in base level can be used to discriminate between competing fluvial erosion models. However, some recent studies of bedrock erosion conclude that transient river long profiles can be approximately characterized by a transport‐limited erosion model, while other authors suggest that a detachment‐limited model best explains their field data. The difference is thought to be due to the relative volume of sediment being fluxed through the fluvial system. Using a pragmatic approach, we address this debate by testing the ability of end‐member fluvial erosion models to reproduce the well‐documented evolution of three catchments in the central Apennines (Italy) which have been perturbed to various extents by an independently constrained increase in relative uplift rate. The transport‐limited model is unable to account for the catchments’response to the increase in uplift rate, consistent with the observed low rates of sediment supply to the channels. Instead, a detachment‐limited model with a threshold corresponding to the field‐derived median grain size of the sediment plus a slope‐dependent channel width satisfactorily reproduces the overall convex long profiles along the studied rivers. Importantly, we find that the prefactor in the hydraulic scaling relationship is uplift dependent, leading to landscapes responding faster the higher the uplift rate, consistent with field observations. We conclude that a slope‐ dependent channel width and an entrainment/erosion threshold are necessary ingredients when modeling landscape evolution or mapping the distribution of fluvial erosion rates in areas where the rate of sediment supply to channels is low

A Multidisciplinary Approach to Pancreas Cancer in 2016: A Review

In this article, we review our multidisciplinary approach for patients with pancreatic cancer. Specifically, we review the epidemiology, diagnosis and staging, biliary drainage techniques, selection of patients for surgery, chemotherapy, radiation therapy, and discuss other palliative interventions. The areas of active research investigation and where our knowledge is limited are emphasized