We propose a novel, efficient, and powerful methodology to deal with overdispersion, excess zeros, heterogeneity, and spatial correlation. It is based on the combination of Hurdle models and Spatial filtering Moran eigenvectors. Hurdle models are the best option to manage the presence of overdispersion and excess of zeros, separating the model into two parts: the first part models the probability of the zero value, and the second part models the probability of the non-zero values. Finally, gathering the spatial information in new covariates through a spatial filtering Moran vector method involves spatial correlation and spatial heterogeneity to improve the model fitting and explain spatial effects of variables that were not possible to measure. Thus, our proposal adapts usual regression models for count data so that it is possible to deal with phenomena where the usual theoretical assumptions, such as constant variance, independence, and unique distribution are not fulfilled. In addition, this research shows how a prolonged armed conflict can impact the health of children. The data includes children exposed to armed conflict in Colombia, a country enduring a non-international armed conflict lasting over 60 years. The findings indicate that children exposed to high levels of violence, as measured by the armed conflict index, demonstrate a significant association with the incidence and mortality rate of LAP in children. This fact is illustrated here using one of the most catastrophic conditions in childhood, as is Pediatric Acute Leukemia (LAP). The association between armed conflict and LAP has its conceptual basis in the epidemiology literature, given that, the incidence and mortality rates of neoplastic diseases increase with exposure to toxic and chronic stress during gestation and childhood. Our methodology provides a valuable framework for complex data analysis and contributes to understanding the health implications in conflict-affected regions.

We propose a novel alternative to generate and classify electroencephalography images, involving the spatial correlation structure among the functional brain-signals. We combine spatial functional methods with image classification techniques to carry out supervised classification of a new silent speech thought, in one specific option that determines an action of a hand prosthesis. Using functional kriging, we find the optimized prediction of the brain signals on unsampled brain locations. Based on these spatial functional predictions, we build a sequence of images in the frequency domain, and then we apply machine learning and deep learning algorithms to this sequence of images. The proposal is applied to a dataset of electroencephalography signals collected on the language area of the brain while people are thinking in silence on each of the five vowels in the Spanish language. Our methodology is implemented in real-life computer interface machines using Google Compute Engine, testing the period for the machine calibration and training the classification algorithm.