SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. Empirical evidence points to the prominent roles of innate immune and CD8 T-cell responses in determining the outcomes. However, how these immune arms act in concert to elicit the outcomes remains unclear. Here, we developed a mathematical model of within-host SARS-CoV-2 infection that incorporates the essential features of the innate immune and CD8 T-cell responses. Remarkably, by varying the strengths and timings of the two immune arms, the model recapitulated the entire spectrum of outcomes realized. Furthermore, model predictions offered plausible explanations of several confounding clinical observations, including the occurrence of multiple peaks in viral load, viral recrudescence after symptom loss, and prolonged viral positivity. We applied the model to analyze published datasets of longitudinal viral load measurements from patients exhibiting diverse outcomes. The model provided excellent fits to the data. The best-fit parameter estimates indicated a nearly 80-fold stronger innate immune response and an over 200-fold more sensitive CD8 T-cell response in patients with mild compared to severe infection. These estimates provide quantitative insights into the likely origins of the dramatic inter-patient variability in the outcomes of SARS-CoV-2 infection. The insights have implications for interventions aimed at preventing severe disease and for understanding the differences between viral variants.
SARS-CoV-2 infection results in highly heterogeneous outcomes, from cure without symptoms to acute respiratory distress and death. While immunological correlates of disease severity have been identified, how they act together to determine the outcomes is unknown. Here, using a new mathematical model of within-host SARS-CoV-2 infection, we analyze diverse clinical datasets and predict that a subtle interplay between innate and CD8 T-cell responses underlies disease heterogeneity. Our model considers essential features of these immune arms and immunopathology from cytokines and effector cells. Model predictions provided excellent fits to patient data and, by varying the strength and timing of the immune arms, quantitatively recapitulated viral load changes in mild, moderate, and severe disease, and death. Additionally, they explained several confounding observations, including viral recrudescence after symptom loss, prolonged viral positivity before cure, and mortality despite declining viral loads. Together, a robust conceptual understanding of COVID-19 outcomes emerges, bearing implications for interventions.
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