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The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques. 

Imbalanced data, a common challenge encountered in statistical analyses of clinical trial datasets and disease modeling, refers to the scenario where one class significantly outnumbers the other in a binary classification problem. This imbalance can lead to biased model performance, favoring the majority class, and affecting the understanding of the relative importance of predictive variables. Despite its prevalence, the existing literature lacks comprehensive studies that elucidate methodologies to handle imbalanced data effectively. In this study, we discuss the binary logistic model and its limitations when dealing with imbalanced data, as model performance tends to be biased towards the majority class. We propose a novel approach to addressing imbalanced data and apply it to publicly available data from the VITAL trial, a large-scale clinical trial that examines the effects of vitamin D and Omega-3 fatty acid to investigate the relationship between vitamin D and cancer incidence in sub-populations based on race/ethnicity and demographic factors such as body mass index (BMI), age, and sex. Our results demonstrate a significant improvement in model performance after our undersampling method is applied to the data set with respect to cancer incidence prediction. Both epidemiological and laboratory studies have suggested that vitamin D may lower the occurrence and death rate of cancer, but inconsistent and conflicting findings have been reported due to the difficulty of conducting large-scale clinical trials. We also utilize logistic regression within each ethnic sub-population to determine the impact of demographic factors on cancer incidence, with a particular focus on the role of vitamin D. This study provides a framework for using classification models to understand relative variable importance when dealing with imbalanced data. 

A growing concern of climate change and waste pollution is causing a shift in products towards green materials. The automotive industry is exploring environmentally friendly alternatives to glass fibers (GF). This research focuses on understanding interactions between constituents of biocomposites made up of basalt fiber (BF) and hemp hurd particle fiber (HF) reinforced polypropylene (PP), and statistically comparing the mechanical properties. The addition of a coupling agent has significantly improved the performance and fiber-matrix interactions in the biocomposite blends. The elastic modulus of some BF/HF/PP mixtures were comparable to the GF/PP composite; however, the GF still outperformed in strength. Rotational and capillary rheometer analysis determined the viscosities of all formulations displaying that basalt composites were consistently lower in viscosity than the glass fiber composite, indicating easier processing conditions. 

Abstract Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini‐based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly‐available benchmark data for the default value, the Gini coefficient method, and the elbow method.