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:
  1. 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.
  2. 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].
  3. 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].
  4. 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.
  5. 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].
  6. 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].
  7. 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].
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