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Gradual loss of water resource in the Salton Sea has got much attention from researchers recently for its damage to the local environment and ecosystems for human beings, animals and plants. To monitor the water resource of the lake, researchers usually obtain certain water resource indices manually from databases such as the satellite images of the region. In this paper, we develop a new method to monitor the area of the Salton Sea automatically. By this method, the lake boundary is first segmented from each satellite image by an image segmentation procedure, and then its area is computed by a numerical algorithm. The sequence of lake areas computed from satellite images taken at different time points is then monitored by a control chart from the statistical process control literature. Because the lake area changes gradually over time, the control chart designed for detecting process mean drifts is used here for the water resource surveillance application. [ABSTRACT FROM AUTHOR]

Point sources in spatially referenced data can impact outcomes in surrounding locations (e.g., a factory that emits air pollution). Previous statistical methods have sought to describe the non-stationary correlation induced by the presence of a point source, with fewer attempting to quantify its overall impact. We introduce directionally varying change points (DVCP), a model that aims to estimate the magnitude of the impact of a point source as well as its range of influence across the spatial domain. DVCP allows for a unique change point parameter, describing the range of influence of a point source, at every angle extending from the source and uses a Gaussian process with directionally defined correlation function to facilitate estimation of the parameters. The Gaussian predictive process approximation is used for fitting the model to large datasets. Through simulation, we show that DVCP can easily accommodate a wide range of shapes defining the range of influence. We apply DVCP to better understand spatial patterns of ambient PM 2.5 concentrations and issues related to environmental inequity in California and Colorado. The method is available in the R package DVCP. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Exploring spatial patterns in the context of disease mapping is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. In most cases, multiple count responses (corresponding to disease incidences of multiple types, such as cancer in men and women) are recorded at each spatial location, which may exhibit similar spatial patterns in addition to disease-specific patterns. These are typically modeled using multivariate shared component models, where the spatial (random) effects may be shared between the disease types to model their association. However, this framework is not immune to spatial confounding, where the latent correlation between the spatial random effects and the fixed effects often leads to misleading interpretation. A recent approach to attenuate spatial confounding is the "SPatial Orthogonal Centroid 'K'orrection", aka SPOCK, which displaces the geographical centroids, ensuring orthogonality of the spatial random effects and the fixed effects. In this paper, we introduce MSPOCK, or Multiple SPOCK, to tackle spatial confounding for the multiple counts scenario. The methodology is evaluated on synthetic data, and illustrated via an application to new cases of respiratory system cancer for men and women for the US state of California in 2016. Our studies show that the MSPOCK correction leads to a reduction of the posterior variance estimates of model parameters, while maintaining the interpretation of the model. [ABSTRACT FROM AUTHOR]

Disease surveillance systems are crucial to monitor and predict outbreaks, epidemics and pandemics, as well as to understand the dynamics and trends of diseases over space and time. For zoonotic diseases, i.e., diseases that spread from animals to humans, surveillance systems often rely on complex data collection mechanisms which present particular challenges to the statistician, including sampling processes that commonly violate key assumptions of standard statistical methods. One such mechanism is preferential sampling, referring to a stochastic dependency between a spatial process of interest and the locations at which it is observed, commonly arising out of practical considerations related to a limited sampling budget and a rare outcome. While this sampling strategy can lead to considerably biased spatial predictions, few solutions to address preferential sampling have been proposed in the context of disease surveillance. We propose a novel approach to correct for preferential sampling in disease surveillance applications and show by simulation the practical benefits of reduced bias in parameter estimates and greater accuracy of the estimated risk surface. We conclude with an application of the model to a disease surveillance dataset targeting plague (Yersinia pestis) in the sylvatic rodent populations in California. [ABSTRACT FROM AUTHOR]