The COVID-19 pandemic underscored the importance of modeling frameworks that integrate biological mechanisms with heterogeneous social contact patterns to accurately characterize variant-specific transmission. Motivated by a One Health perspective that connects human infection biology, behavioral dynamics, and environmental transmission factors, we present a data-integrated and mechanistic approach designed to support proactive risk assessment and public-health preparedness. While classical compartmental models offer essential baseline insight, their simplifying assumptions limit the representation of time-varying infectiousness and realistic transmission heterogeneity. We introduce a multi-scale agent-based model that links empirically inferred SARS-CoV-2 viral kinetics to population-level spread through a mechanistic mapping from viral load to infection probability. Ct trajectories are estimated using hierarchical Bayesian inference and incorporated into a structured contact network, enabling coupling of within-host viral dynamics with social interaction patterns. This One Health-aligned modeling architecture supports rigorous data integration and biologically grounded estimation of variant-specific epidemic behavior. Our results demonstrate that differences in viral kinetics substantially reshape epidemic trajectories. Variants with rapid viral expansion and short infectious periods produce earlier and sharper peaks, whereas slower proliferation and prolonged clearance lead to delayed yet larger outbreaks. Incorporating time-varying infectiousness also generates heterogeneous secondary-case distributions and occasional high-impact transmission events without imposing ad-hoc superspreading parameters, highlighting biological drivers of overdispersion. By linking within-host viral dynamics to network-level transmission, this framework provides a scalable tool for variant surveillance, quantitative risk assessment, and timing-sensitive intervention planning. It can be extended to environmentally mediated pathogens, strengthening One Health-oriented data integration and epidemic estimation for future emerging threats.
Lee et al. (Tue,) studied this question.