The impact of COVID-19 disease on health and economy has been global,
and the magnitude of devastation is unparalleled in modern history. Any
potential course of action to manage this complex disease requires the
systematic and efficient analysis of data that can delineate the
underlying pathogenesis. We have developed a mathematical model of
disease progression to predict the clinical outcome, utilizing a set of
causal factors known to contribute to COVID-19 pathology such as age,
comorbidities, and certain viral and immunological parameters. Viral
load and selected indicators of a dysfunctional immune response, such as
cytokines IL-6 and IFN which contribute to the cytokine storm and
fever, parameters of inflammation d-dimer and ferritin, aberrations in
lymphocyte number, lymphopenia, and neutralizing antibodies were
included for the analysis. The model provides a framework to unravel the
multi-factorial complexities of the immune response manifested in
SARS-CoV-2 infected individuals. Further, this model can be valuable to
predict clinical outcome at an individual level, and to develop
strategies for allocating appropriate resources to mitigate severe cases
at a population level.