Decoding Viral Transmission Dynamics in Structured Populations Using a Microfluidic Herd-Immunity-on-a-Chip Platform

Understanding how population structure shapes viral transmission remains challenging because most in vitro models assume well-mixed conditions and lack spatial structure. We present a Herd-Immunity-on-a-Chip (HIC) platform-a 444-chamber microfluidic network that recreates structured populations-and apply it to human coronavirus 229E infecting MRC-5 fibroblasts. By varying initial inoculum (I₀), susceptible density (S₀), cell motility, and the fraction of non-susceptible cells (U₀), we decode how seeding, density, immunity and movement reshape outbreak trajectories. Increasing S₀ or I₀ raised local contact rates, reduced effective intercellular spacing, and increased the reproduction number (R₀), accelerating spread. In contrast, higher U₀ suppressed transmission; with U₀ ≥ 80% of the fixed uninfected population (S₀ + U₀), outbreaks collapsed (herd immunity). Independently, lowering S₀ slowed early spread but did not, alone, prevent eventual transmission. We integrate the data with mathematical modelling of spatial, contact-structured transmission to quantify changes in R₀ and apparent herd-immunity thresholds. HIC offers a generalizable, bench-top framework for outbreak forecasting and intervention testing.