The COViD-19 pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen . In order for public health, care delivery systems, clinicians and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view . To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COViD-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy . Here, we present a novel approach to model COViD-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling . Our process will serve as a methodology for analysis of the data being collected in the ASAIO database, and other data sources worldwide.