When a pathogen is transmitted from an infected individual to a susceptible host, many complex factors influence the outcome of transmission: whether the pathogen will cause disease, what are the lengths of latent and infectious periods, if protective immunity will arise and how long will immunity last (memory). Transmission models of disease are numerous and capture many epidemiological aspects. However, broad individual variability exists, that in classical compartmental models is captured by assuming Poisson processes for transitions between epidemiological states (e.g. infectious to removed). Using multi-scale models that explicitly account for the dynamics of host-specific immune responses operating in the population scale can allow a finer-grained description of outcomes of infection and ultimately a better understanding of transmission dynamics. In this study, we propose a multi-scale model for Mycobacterium Tuberculosis (Mtb), the bacterium that causes tuberculosis (TB). The multi-scale model bridges together two existing models: a socio-demographic, individual based model for TB transmission dynamics, and a mechanistic, validated ODE model for host Mtb immune response, expressly adapted for multi-scale integration. Changes to individual epidemiological status are driven by state variables of host immune response (such as bacterial load), whose value over time is determined in an ODE model by two classes of factors: i) characteristics of the infection episode and ii) host immune specific characteristics. The multi-scale model is calibrated against epidemiological data on TB incidence (both over time and by age) in a low burden demographic setting and the predicted age-specific frequency and timing of infection outcomes is compared with currently used estimates in epidemiological models. Relations between host immune factors, their distribution in the population and relevant aspects of TB epidemic dynamics are identified by multivariate sensitivity analysis. Finally, we discuss possible applications of the model for improved understanding of TB epidemiology and disease control.