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The assessment of water quality across space and time is of considerable interest for both agricultural and public health reasons. The standard method to assess the water quality of a sub-catchment, or a group of sub-catchments, usually involves collecting point measurements of water quality and other additional information such as the date and time of measurements, rainfall amounts, the land use and soil type of the catchment and the elevation. Some of this auxiliary information is point-referenced data, measured at the exact location, whereas other such as land use is areal data often recorded in a compositional format at the catchment or sub-catchment level. The spatial change of support inherited by this data collection process breaks the natural link between the response variable and the predictors. In this paper, we present an approach to reconstruct this link by using a categorical latent variable that identifies the land use that most likely influences water quality in each sub-catchment. This constitutes the spatial clustering layer of the model. Each cluster is associated with an estimated temporal variability common to water quality measurements. The strength of this approach lies in the temporal variation identifying each cluster, allowing decision makers to make inform decision regarding land use and its influence over water quality. We demonstrate the potential of this approach with data from a water quality research study in the Mount Lofty range, in South Australia.

Synthetic gene drives could provide new solutions to a range of old problems such as controlling vector-borne diseases, agricultural pests and invasive species. In this paper, we outline methods to identify hazards and detect potentially adverse ecological outcomes at the individual (genotype, phenotype), population, community and ecosystem level, when progressing Gene Drive Modified Organisms through a phased test and release pathway. We discuss the strengths and weaknesses of checklists and structured hazard analysis techniques, identify methods to help meet some of the challenges of detecting adverse ecological outcomes in experiments and confined field trials, and discuss ways to improve the efficiency and statistical rigour of post-release monitoring strategies.

Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness in the distribution of LOS poses problems in statistical modelling because it fails to adequately follow the usual traditional distribution of positive variables such as the log-normal distribution. We present in this paper a model using the convolution of two distributions, a technique well known in the signal processing community. The specificity of that model is that the variable of interest is considered to be the resulting sum of two random variables with different distributions. One of the variables features the patient-related factors in terms of their need to recover from their admission condition, while the other models the hospital management process such as the discharging process. Two estimation procedures are proposed. One is the classical maximum likelihood, while the other relates to the expectation-maximization algorithm. We present some results obtained by applying this model to a set of real data from a group of hospitals in Victoria (Australia). Copyright © 2016 John Wiley & Sons, Ltd.

The geographic niches of many species are dramatically changing as a result of environmental and anthropogenic impacts such as global climate change and the introduction of invasive species. In particular, genetically compatible subspecies that were once geographically separated are being reintroduced to one another. This is of concern for conservation, where rare or threatened subspecies could be bred out by hybridising with their more common relatives, and for commercial interests, where the stock or quality of desirable harvested species could be compromised. It is also relevant to disease ecology, where disease transmission is heterogeneous among subspecies and hybridisation may affect the rate and spatial spread of disease. We develop and investigate a mathematical model to combine competitive effects via the Lotka-Volterra model with hybridisation effects via mate choice. The species complex is structured into two classes: a subspecies of interest (named x), and other subspecies including any hybrids produced (named y). We show that in the absence of limit cycles the model has four possible equilibrium outcomes, representing every combination: total extinction, x-dominance (y extinct), y-dominance (x extinct), and at most a single coexistence equilibrium. We give conditions for which limit cycles cannot exist, then further show that the “total extinction” equilibrium is always unstable, that y-dominance is always stable, and that the other equilibria have stability depending on the model parameters. We demonstrate that both x-dominance and coexistence are achievable under a wide range of parameter values and initial conditions, which corresponds with empirical evidence of known competing-hybridising systems. We then briefly examine bifurcation behaviour. In particular, we note that a subcritical bifurcation is possible in which a “catastrophic” transition from x-dominance to y-dominance can occur, representing an invasion event. Finally, we briefly examine the common complication of time-varying carrying capacity, showing that such a case can make coexistence more likely.

ObjectiveRecent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a sample of publications.MethodsThe checklist was applied to a sample of 100 academic population health publications distributed among 5 readers. Cohen’s κ was used to measure interrater agreement.ResultsOf the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good.ConclusionThis study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.

The work presented here is concerned with performance analysis for data association, in a target tracking environment. Effects of misassociation are considered in a simple (linear) multiscan framework so as to provide closed-form expressions of the probability of correct association. We focus here on the development of explicit approximations of this probability. Via rigorous calculations the effect of dimensioning parameters (number of scans, false measurement positions or densities) is analyzed, for various modelings of the false measurements. Remarkably, it is possible to derive very simple expressions of the probability of correct association which are independent of the scenario kinematic parameters.

This paper discusses the opportunities big data offers decision makers from a statistical perspective. It calls for a multidisciplinary approach by computer scientists, statisticians and domain experts to providing useful big data solutions. Big data calls for us to think in new ways and communicate effectively within such teams. We make a plea for linking data-driven and model-driven analytics, and stress the role of cause-effect models for knowledge enhancement in big data analytics. We remember Kant’s statement that theory without data is blind, but facts without theories are meaningless. A case is made for each discipline to define the contribution they offer to big data solutions so that effective teams can be formed to improve inductions. Although new approaches are needed much of the past learning related to small data are valuable in providing big data solutions. Here we have in mind the long-term academic training and field experience of statisticians concerning reduction of dataset volumes, sampling in a more general setting, data depreciation and quality, model design and validation, visualisation, etc. We expect that combining the present approaches will give incentives for increasing the chances for “real big solutions”.

Length of hospital stay (LOS) is an important indicator of the hospital activity and management of health care. The skewness in the distribution of LOS poses problems in statistical modelling because it fails to adequately follow the usual traditional distribution of positive variables such as the log-normal distribution. We present in this paper a model using the convolution of two distributions, a technique well known in the signal processing community. The specificity of that model is that the variable of interest is considered to be the resulting sum of two random variables with different distributions. One of the variables features the patient-related factors in terms of their need to recover from their admission condition, while the other models the hospital management process such as the discharging process. Two estimation procedures are proposed. One is the classical maximum likelihood, while the other relates to the expectation-maximization algorithm. We present some results obtained by applying this model to a set of real data from a group of hospitals in Victoria (Australia). Copyright © 2016 John WileySons, Ltd.

The aim of this paper is to present a new algorithm for target tracking within a binary sensor network. The present work is based on our previous results developed in. A novel tracking method is proposed and its performance through a very classical trajectory model is evaluated. For a given target, this algorithm provides an estimation of its velocity and then of its position. The greatest improvements are made through a position correction and velocity analysis.

International audience; In this paper, we present a new methodology for the estimation and the prediction of the concentration of pollutant in a complex environment. We take benefit of a semi-parametric formulation of the problem to perform a faster and more efficient estimation of the pollutant cloud. In a first part, we present how we use the Gaussian process to model the interactions between position and time given the observations. Then, we introduce the expansion as a function of the observations through the time, and we construct an estimator of the time of release from it within change-point detection framework. Then, we use this time estimate to obtain the position (or more likely, a confidence region of the position) of the source. Several simulations are provided in a complex city scenario that demonstrate the accuracy of the proposed technique.