This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.

During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.

In this study, we examine a set of primary data collected from 484 students enrolled in a large public university in the Mid-Atlantic United States region during the early stages of the COVID-19 pandemic. The data, called Ties data, included students’ demographic and support network information. The support network data comprised of information that highlighted the type of support, (i.e. emotional or educational; routine or intense). Using this data set, models for predicting students’ academic achievement, quantified by their self-reported GPA, were created using Chi-Square Automatic Interaction Detection (CHAID), a decision tree algorithm, and cforest, a random forest algorithm that uses conditional inference trees. We compare the methods’ accuracy and variation in the set of important variables suggested by each algorithm. Each algorithm found different variables important for different student demographics with some overlap. For White students, different types of educational support were important in predicting academic achievement, while for non-White students, different types of emotional support were important in predicting academic achievement. The presence of differing types of routine support were important in predicting academic achievement for cisgender women, while differing types of intense support were important in predicting academic achievement for cisgender men.