GL22

The dynamics of Mycobacterium tuberculosis propagation in human populations presents a series of distinctive features that turns the development of accurate transmission models into an especially challenging task. For example, the slow evolution and big inertia of tuberculosis burden trends -that are a consequence of the large prevalence levels of latent tuberculosis infection- demand for model-based simulations that often span for several decades. This fact, coupled with the strong etary dependencies that are observed in many key epidemiological parameters, evidence the need of integrating suitable descriptions of demographic evolution into transmission models. This is because, in many geographical settings, the demographic structure of the populations is simultaneously a quickly evolving feature and an impactful factor in shaping the transmission dynamics of the pathogen.

In this talk, I will summarize our group's efforts to integrate data-driven descriptions of demographic evolution within TB transmission models, paying special attention to the role of age-mixing patterns and their dependency on (evolving) demographic structures.

Our findings demonstrate that the inclusion of demographic dynamics into the mathematical models we use to produce baseline forecasts for TB burden series, as well as prospective evaluations for epidemiological interventions significantly impact the conclusions of many of these analyses. To illustrate this, we will discuss how the application of these modeling concepts to practical scenarios—such as assessing the expected impact of assorted vaccination campaigns in China, a country characterized by high TB burden levels and rapid demographic aging— yields markedly different implications for public health strategies.