Speaker
Description
Influenza infections exhibit substantial heterogeneity in viral shedding and
immune responses, along with varying widely in severity and outcome. While viral load
dynamics and certain immune responses are often assumed to correlate with disease
severity, experimental observations suggest that these relationships are not always
straightforward and can change across the duration of infection. To investigate the
multivariate sources of this variability and how it relates to severity, we turn to
mechanistic modeling. We apply a system of differential equations describing influenza
viral replication and CD8+ T cell dynamics to human challenge data. Model parameters
are estimated across individuals to characterize meaningful variability that governs
infection kinetics. Through model-based analysis and clustering of infection trajectories,
we identify multivariate drivers that contribute to heterogeneous infection courses. We
further extend the model to incorporate symptom development and a wide range of data
sources, including human clinical data as well as additional immune cell and cytokine
data. These results highlight the value of mechanistic modeling for disentangling the
biological processes underlying variable immune responses and provide a framework
for improving the predictability of influenza infection dynamics.
