Bridging spatial scales in respiratory viral infection

Respiratory viruses spread within the host through both local expansion and occasional long-range dissemination that seeds new infection foci. We present LEAP, an analytically tractable within-host model that captures this two-scale process by coupling local plaque growth to long-range seeding. The model reduces to an age-dependent branching process and yields a closed-form expression for the exponential growth rate during early infection. Using empirical data to parametrize the model, we find that productive dissemination requires only a small number of successful long-range seeding events per infected cell, with distinct values for SARS-CoV-2 and influenza A virus. LEAP further predicts that, in these well-adapted viruses, interferon-mediated restriction only weakly affects exponential growth, while remaining decisive for poorly adapted ones. More broadly, the model provides a flexible framework for experimentally testable predictions of early infection dynamics. Significance StatementRespiratory viral infection is an inherently spatial process, in which the virus must colonize large areas of the airways to optimize reproduction. Recent studies in animal models infected with influenza A or SARS-CoV-2 have documented long-range stochastic jumps of viral populations between distant regions of the respiratory tract. The emerging picture is one of two co-occurring spatial processes: slow, local plaque expansion and long-range seeding events that are rare but crucial to rapid colonization of the airways. We introduce LEAP (Lotka-Euler Airway Pathogen model), a simple mathematical within-host model that captures these two-scale dynamics by coupling local plaque growth to stochastic long-range seeding. Using LEAP, measurements from the petri dish can directly produce predictions of infection dynamics in the body.