A data-driven model for the assessment of Tuberculosis transmission in evolving demographic structures. (PNAS, 2018)

Although TB is a strongly age-dependent disease, it remains unclear how TB epidemics will respond, in the following decades, to the global aging that human populations are experiencing all around the world. This  is partly caused by the limitations of current epidemiological models at capturing the coupling between demographic dynamics and TB transmission. Here, we present a data-driven transmission model that, unlike previous approaches, explicitly contemplates relevant aspects of the relation between age structure and TB dynamics, such as demographic evolution and contact heterogeneities. The over-simplified description of these aspects that previous approaches proposed introduces very significant biases on model forecasts of TB burden that translate into a systematic under-estimation of future burden trends in many countries, as we show here.

  • Arregui, S., Marinova, D., Iglesias, M. J., Samper, S., Martin, C., Sanz*, J. & Moreno*, Y. (2018). A data-driven model for the assessment of M. tuberculosis transmission in evolving demographic structures. *=co-last author. PNAS, 2018 (article)

 

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