Predicting heterogeneous CD8+ immune memory responses in COVID-19 using a virtual patient cohort
Throughout the COVID-19 pandemic, considerable efforts have been made to understand the mechanisms of the immune response to SARS-CoV-2 infections and discover the key factors causing heterogeneous COVID-19 responses. Moreover, durable viral immunity to SARS-CoV-2 generated after infection in naïve hosts or after vaccination is of high importance, given its implication for protection from severe disease. To better understand the formation and function of cellular immunological memory after SARS-CoV-2 infection, we developed a mechanistic, computational model of COVID-19 immunopathology that explicitly describes the interactions between epithelial cells, innate and adaptive immune cells, and cytokines, as well as the differentiation process of naïve CD8+ T cells into effector and memory cell subsets. Through calibration and validation against a broad range of experimental and clinical immunological data, we studied heterogeneity in CD8+ T cell-mediated immunity to SARS-CoV-2. Using our model, we identified key mechanisms distinguishing COVID-19 severity and reducing inflammation in reinfections using a virtual patient cohort. These include the monocyte-to-macrophage differentiation rate, the IFN production rate by infected cells, and the monocyte recruitment rate. Our results show that memory CD8+ T cell generation is critical to offering durable protection against severe COVID-19 and reduces heterogeneous outcomes upon re-exposure to the same virus. We can also apply our model to predict the humoral immune response post-vaccination to study breakthrough infections. Thus, this work provides a platform for investigating key questions about heterogeneity in the response to SARS-CoV-2.