From Virus to Population: Multi-Scale Stochastic Modeling Reveals Mutation-Driven Acceleration of Infection Dynamics

Predicting the course of epidemics and developing effective interventions depend on an understanding of how viral mutations affect infection dynamics at various biological scales. Here, we provide a thorough in silico investigation into the effects of viral mutations on infection progression by integrating population-level SIR modeling, immune response, multi-strain dynamics, and stochastic intracellular viral simulations. Stochastic simulations demonstrated that even slight mutation-induced increases in viral production or infectivity at the intracellular level resulted in significantly greater peaks in the number of infected cells. The amplification potential of minor genetic alterations was demonstrated by the fact that wildtype strains attained an average peak of around 2,680 infected cells, whereas mutant strains had an average peak of about 8,070 cells. The significance of random fluctuations in early infection dynamics was underscored by the models' capacity to capture underlying stochastic variability. It was discovered that mutations that accelerated intracellular infection directly translated into larger and faster epidemics when the intracellular viral load was coupled to a population-level SIR framework. In particular, mutant strains raised the effective reproduction number (R0) from 3.0 to 4.5, resulting in an earlier epidemic peak at about 24 days and a peak infected individual count of about 4,430. The robustness of these effects was further shown by sensitivity studies, which showed that infection peaks were extremely responsive to variations in viral generation and infectivity. All together, our multi-scale modeling methodology shows how minor cellular-level mutation-induced alterations can spread to affect epidemic outcomes at the population level. The significance of stochasticity in infection dynamics, the potential for early intervention measures to reduce intracellular viral amplification and epidemic development, and mechanistic insights into viral evolution are all highlighted by these findings. A strong computational tool for investigating the effects of viral mutations and guiding public health interventions is provided by this integrated platform.