000127820 001__ 127820
000127820 005__ 20241125101158.0
000127820 0247_ $$2doi$$a10.1038/s41467-023-40976-6
000127820 0248_ $$2sideral$$a134940
000127820 037__ $$aART-2023-134940
000127820 041__ $$aeng
000127820 100__ $$aTovar, M.$$uUniversidad de Zaragoza
000127820 245__ $$aAddressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines
000127820 260__ $$c2023
000127820 5060_ $$aAccess copy available to the general public$$fUnrestricted
000127820 5203_ $$aIn tuberculosis (TB) vaccine development, multiple factors hinder the design and interpretation of the clinical trials used to estimate vaccine efficacy. The complex transmission chain of TB includes multiple routes to disease, making it hard to link the vaccine efficacy observed in a trial to specific protective mechanisms. Here, we present a Bayesian framework to evaluate the compatibility of different vaccine descriptions with clinical trial outcomes, unlocking impact forecasting from vaccines whose specific mechanisms of action are unknown. Applying our method to the analysis of the M72/AS01E vaccine trial -conducted on IGRA+ individuals- as a case study, we found that most plausible models for this vaccine needed to include protection against, at least, two over the three possible routes to active TB classically considered in the literature: namely, primary TB, latent TB reactivation and TB upon re-infection. Gathering new data regarding the impact of TB vaccines in various epidemiological settings would be instrumental to improve our model estimates of the underlying mechanisms.
000127820 536__ $$9info:eu-repo/grantAgreement/ES/DGA/B49-23R-NeuroBioSys$$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL$$9info:eu-repo/grantAgreement/ES/DGA-IIU/796-2019$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-106859GA-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-115800GB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/RYC-2017-23560
000127820 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000127820 590__ $$a14.7$$b2023
000127820 592__ $$a4.887$$b2023
000127820 591__ $$aMULTIDISCIPLINARY SCIENCES$$b8 / 134 = 0.06$$c2023$$dQ1$$eT1
000127820 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2023$$dQ1
000127820 593__ $$aPhysics and Astronomy (miscellaneous)$$c2023$$dQ1
000127820 593__ $$aChemistry (miscellaneous)$$c2023$$dQ1
000127820 594__ $$a24.9$$b2023
000127820 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000127820 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Y.$$uUniversidad de Zaragoza
000127820 700__ $$0(orcid)0000-0002-2980-9685$$aSanz, J.$$uUniversidad de Zaragoza
000127820 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000127820 773__ $$g14, 5312 (2023), 12$$tNature communications$$x2041-1723
000127820 8564_ $$s2774407$$uhttps://zaguan.unizar.es/record/127820/files/texto_completo.pdf$$yVersión publicada
000127820 8564_ $$s2670584$$uhttps://zaguan.unizar.es/record/127820/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000127820 909CO $$ooai:zaguan.unizar.es:127820$$particulos$$pdriver
000127820 951__ $$a2024-11-22-12:10:15
000127820 980__ $$aARTICLE