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The BCG vaccine remains the only licensed vaccine against tuberculosis (TB), yet its protective efficacy is highly variable, and the mechanisms behind BCG-induced protection remain poorly understood. Plumlee et al. (PLOS Pathogens 2023) infected over 1,000 mice, half of which were vaccinated with BCG, with an ultra low dose (ULD) of Mycobacterium tuberculosis (Mtb); the authors found that BCG vaccination resulted in fewer infected mice, lower CFU lung burden, and more frequent unilateral lung infection. We have developed several mathematical models of Mtb dynamics and dissemination between murine lungs and fit these models to the CFU data from unvaccinated or BCG-vaccinated mice. Alternative mathematical models incorporating either direct (lung-to-lung) or indirect (lung-intermediate-tissue-lung) dissemination pathways fit the unvaccinated data equally well, suggesting multiple plausible routes of Mtb spread. Yet, irrespective of the dissemination route, the models predicted rapid Mtb replication during early infection, transient control within 1–2 months after infection, and continued bacterial growth in the chronic phase. Fitting models to the data from BCG-vaccinated animals revealed that BCG reduces the rate of Mtb dissemination by 89% while having a more modest effect on the replication rate within the lung, reducing it by 9%. Stochastic simulations incorporating random infection and dissemination between the right lung and left lung explained the observed variability in experimentally measured CFUs well. We used our parameterized mathematical models to calculate the number of mice needed to detect the efficacy of a hypothetical vaccine on the probability of Mtb clearance or dissemination between murine lungs that extends previously provided estimates. Taken together, our novel mathematical modeling-based framework provides a rigorous way for quantifying vaccine efficacy in ULD-infected mice, paving the way for the pre-clinical evaluation of next-generation TB vaccines.
