INTRODUCTION
The COVID-19 pandemic caused by infection with SARS-CoV-2 was officially
announced in March 2020 by the CDC and WHO [1, 2]. As of this
publication, more than 100 million infections and over 2.6 million
deaths have been reported worldwide. Majority of the subjects have
asymptomatic infections. The rate of fatality is disproportionately high
in the elderly and patients with comorbidities such as diabetes, cardiac
disease, and kidney disease [3, 4]. The consequences of the pandemic
are fraught with potential loss of lives, social and economic distress,
and the uncertainty of disease progression because of variable
individual pathogenesis.
A unique and dysregulated immune response has been shown to be a
hallmark of COVID-19 [5-9]. Figure 1 schematically depicts
the cascade of events that contribute to the progression of disease.
Mathematical models have been utilized by several investigators to
understand the mechanisms of disease pathogenesis, immune pathways
involved and course of viral infections [10, 11]. In this article,
we have proposed a predictive model that utilizes the levels of clinical
and laboratory parameters to determine the severity of clinical outcomes
ranging from asymptomatic to mild, moderate, severe, and critical
disease states. The proposed model can be useful to predict clinical
outcome at the individual-level and develop efficient and effective
treatment strategies to manage public health challenges at the
population-level.
The questions the model attempts to answer are: i) At an individual
level, what is the probability of an individual infected with
SARS-CoV-2, given the clinical signs and laboratory values on various
days, likely to progress to severe disease, and ii) At a population
level, what are the prioritized clinical and laboratory parameters that
are most likely to contribute to progression to severe disease. We have
used a multiple regression based model to predict severity of the
outcome of COVID-19. To evaluate the combinatorics that are not observed
in the sample, we have applied resampling methods based on Monte Carlo
simulation.