RESULTS
We evaluated multiple approaches to develop mathematical models using parameters that can predict the progression of disease. Candidate parameters were selected from mechanistic understanding of the process of pathogenesis of COVID-19 to evaluate their possible impact on the clinical outcome. Regression models utilize data to build predictive models. Hypotheses are examined and confirmed with pre-determined statistical confidence and inferential power. These models incorporate all the experimental variability in the data set. Since the models contained numeric factors and numeric ordinal outcomes, we utilized methods of Multiple Linear Regression [41]. In this approach, we used the simulated data set from COVID-19 affected subjects, organized, and analyzed it to understand the variability of each of the parameters.