Speaker
Description
Early SARS-CoV-2 infection is governed by nonlinear and often antagonistic interactions among immune cells, cytokines, and intracellular signaling pathways. We developed a mechanistic agent-based model (ABM) of early lung infection integrating pneumocytes, macrophages, natural killer (NK) cells, type-I interferon (IFN) signaling, and the mTORC1 inhibitor sirolimus. To enable large-scale analysis, we trained a Deep Gaussian Process (DGP) surrogate on ABM simulations and generated a virtual patient population spanning physiologically plausible parameter ranges. Using Morris and functional ANOVA sensitivity analyses, we identified regime-dependent drivers of infection outcomes at 48 hours post-infection. When IFN-mediated viral inhibition spans a wide range, viral replication kinetics dominate system behavior, and sirolimus strongly reduces viral load and inflammation despite immunosuppressive effects. In contrast, when IFN inhibition is highly effective, IFN secretion rate becomes the primary determinant of viral load, and sirolimus has diminished impact. The model further predicts context-dependent roles for macrophage polarization, reconciling conflicting experimental findings. These results highlight how immune–viral feedback structure determines therapeutic efficacy and underscores the value of surrogate-assisted ABM sensitivity analysis for interpreting heterogeneous treatment responses.
