Abstract: The present disclosure provides systems and methods for determining the presence and severity of sleep disordered breathing in a patient based on the output of a low-cost at-home diagnostic and the results of a health questionnaire. The low-cost at-home diagnostic is a simple photoplethysmographic survey to detect oxygen saturation overnight. Minimum oxygen saturation and other metrics are determined from the photoplethysmographic survey and applied, in combination with the health questionnaire data, to a set of artificial neural networks. Each artificial neural network corresponds to a respective degree of severity of sleep disordered breathing, according to rate of occurrence of apnea and hypopnea events during sleep. Each artificial neural network is trained with a respective subset of clinical data generated from a large population of individuals, to reduce both the false positive and false negative rate of the classifier.

Abstract: The present invention provides a system and method to geospatially and temporally predict a propagation event. The present invention for a plurality of predetermined locations, geospatially models the connections between each location. For each predetermined location, the invention temporally models the connections within each predetermined location. The present invention also pairs the geospatially modeling with the temporal modeling to generate a prediction of the spread of the propagation event.