Bridging the gap between efficacy trials and model-based impact evaluation for new tuberculosis vaccines

Mario Tovar, Sergio Arregui, Dessislava Marinova, Carlos Martín, Joaquín Sanz () and Yamir Moreno ()
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Mario Tovar: Institute for Bio-computation and Physics of Complex Systems (BIFI), University of Zaragoza
Sergio Arregui: Institute for Bio-computation and Physics of Complex Systems (BIFI), University of Zaragoza
Dessislava Marinova: University of Zaragoza
Carlos Martín: University of Zaragoza
Joaquín Sanz: Institute for Bio-computation and Physics of Complex Systems (BIFI), University of Zaragoza
Yamir Moreno: Institute for Bio-computation and Physics of Complex Systems (BIFI), University of Zaragoza

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract In Tuberculosis (TB), given the complexity of its transmission dynamics, observations of reduced epidemiological risk associated with preventive interventions can be difficult to translate into mechanistic interpretations. Specifically, in clinical trials of vaccine efficacy, a readout of protection against TB disease can be mapped to multiple dynamical mechanisms, an issue that has been overlooked so far. Here, we describe this limitation and its effect on model-based evaluations of vaccine impact. Furthermore, we propose a methodology to analyze efficacy trials that circumvents it, leveraging a combination of compartmental models and stochastic simulations. Using our approach, we can disentangle the different possible mechanisms of action underlying vaccine protection effects against TB, conditioned to trial design, size, and duration. Our results unlock a deeper interpretation of the data emanating from efficacy trials of TB vaccines, which renders them more interpretable in terms of transmission models and translates into explicit recommendations for vaccine developers.

Date: 2019
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DOI: 10.1038/s41467-019-13387-9

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