On particle dispersion statistics using unsupervised learning and Gaussian mixture models

Abstract

Understanding the dispersion of particles in enclosed spaces is crucial for controlling the spread of infectious diseases. This study introduces an innovative approach that combines an unsupervised learning algorithm with a Gaussian mixture model to analyze the behavior of saliva droplets emitted from a coughing individual. The algorithm effectively clusters data, while the Gaussian mixture model captures the distribution of these clusters, revealing underlying sub-populations and variations in particle dispersion. Using computational fluid dynamics simulation data, this integrated method offers a robust, data-driven perspective on particle dynamics, unveiling intricate patterns and probabilistic distributions previously unattainable. The combined approach significantly enhances the accuracy and interpretability of predictions, providing valuable insights for public health strategies to prevent virus transmission in indoor environments. The practical implications of this study are profound, as it demonstrates the potential of advanced unsupervised learning techniques in addressing complex biomedical and engineering challenges and underscores the importance of coupling sophisticated algorithms with statistical models for comprehensive data analysis. The potential impact of these findings on public health strategies is significant, highlighting the relevance of this research to real-world applications.