Rationale for the parameters included in the analysis
The input parameters selected for this model, which requires cause
(clinical and laboratory parameters) and effect (clinical outcome)
relationships, were based on the data reported in recent scientific
publications. Figure 1 shows the schematic representation of
the stage of disease progression and parameters associated with the
increasing severity of diseases. The following parameters were chosen:
- Comorbidities: Though the precise mechanism(s) of disease
progression in patients with comorbidities has yet to be elucidated,
pre-existing conditions such as diabetes, cancer, neurological,
cardiac and lung and kidney disease have been reported to contribute
towards severity of COVID-19 [16, 17]. The simulated data for
comorbidity was generated using an arbitrary range of 1 to 4, where 1
represented a healthy individual and 4 represented an individual with
a severe co-morbidity.
- Age: A range of 18 to 100 years was utilized for generating
the mock data set. The assumption used in generating the data was that
disease progression was directly proportional to age [17]. Reports
of certain rare pathogenic conditions in children, e.g., Kawasaki
disease [18, 19], have not been considered in the current model.
Reports indicate that majority of children infected with SARS-CoV-2
are asymptomatic [19].
- Viral load: SARS-CoV-2 infects individuals through the
nasopharyngeal pathway. This infection is the cause of all subsequent
effects. Viral load is measured by reverse-transcriptase quantitative
PCR (RT-qPCR), which detects viral RNA from nasopharyngeal swabs
[20]. The test relies on multiple cycles of RNA amplification to
produce detectable amount of RNA in the mixed nucleic acid sample,
reflected in the Cycle-time (Ct) value, which is defined as the number
of cycles necessary to detect the virus. A Ct value of less than 20 is
considered a high viral load while a Ct value of 35 and higher
indicates a lower level or near absence of viral infection [20].
Viral load in patients is dependent on various factors, including
number of ACE2 and TMPRSS2 receptors [21, 22], comorbidities,
cytokines, number of viral particles at infection, and the overall
immune health status of the patients [23-26]. Viral loads have
been demonstrated to have a direct correlation with severity of
disease and mortality in COVID-19 [27, 28].
- Cytokine Storm: High viral loads evoke defensive mechanisms
that can induce inflammation leading to a dysregulated innate immune
response that could result in a cytokine storm characterized by
fever-inducing levels of cytokines such as IL6, IFNα, IL1β and CXCL-10
[27, 29-33]. CXCL-10, interestingly was also found to be
indicative of severe outcomes in patients affected by the SARS CoV1
outbreak in 2002 [34]. Cytokine storm has been implicated in
contributing to pulmonary immunopathology, leading to severe clinical
disease and mortality. In this model, we have included levels of IFNα
and IL6 obtained from the published data.
- Systemic Inflammation: Laboratory based parameters indicating
inflammation in the serum, such as D-Dimer and Ferritin, have been
shown to lead to a reduction in blood oxygen saturation levels,
reflecting inadequate oxygenation in the lungs [35, 36].
- Lymphopenia: Viral infection can lead to marked lymphopenia
that can affect both CD4+ and CD8+ T cells [3, 28, 36].
Lymphopenia, reflected by significantly reduced CD4 and CD8 T cells in
peripheral blood, is likely due to sequestration and cell death and
reflected by significantly reduced CD4 and CD8 T cells in peripheral
blood, has been reported in moderate and severe COVID-19 patients. In
addition, antigen specific CD8 Cytotoxic T lymphocyte (CTL) responses
have been detected approximately a week following viral infection, and
the magnitude of the response was observed to have protective or
damaging effects [37].
- Neutralizing antibodies: Neutralizing antibodies bind to
specific surface receptors on infectious agents such as viruses and
toxins, reducing or eliminating their ability to exert harmful effects
on cells. SARS-CoV-2 infected individuals generate a robust and
long-lasting neutralizing antibody response, and plasma from
convalescent COVID-19 patients has been used for treatment of severe
disease with some success [38, 39]. It has recently been reported
that neutralizing antibodies to SARS-CoV-2 can predict severity and
survival, with higher titers being associated with severe disease in
some instances [40].