Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines
Abstract In 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-40976-6 |
| _version_ | 1797558087384563712 |
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| author | M. Tovar Y. Moreno J. Sanz |
| author_facet | M. Tovar Y. Moreno J. Sanz |
| author_sort | M. Tovar |
| collection | DOAJ |
| description | Abstract In 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. |
| first_indexed | 2024-03-10T17:26:33Z |
| format | Article |
| id | doaj.art-93dd5ffef79d4f88a19ad5d1ae8431fc |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-10T17:26:33Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-93dd5ffef79d4f88a19ad5d1ae8431fc2023-11-20T10:10:30ZengNature PortfolioNature Communications2041-17232023-09-0114111210.1038/s41467-023-40976-6Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccinesM. Tovar0Y. Moreno1J. Sanz2Institute for Biocomputation and Physics of Complex Systems (BIFI), University of ZaragozaInstitute for Biocomputation and Physics of Complex Systems (BIFI), University of ZaragozaInstitute for Biocomputation and Physics of Complex Systems (BIFI), University of ZaragozaAbstract In 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.https://doi.org/10.1038/s41467-023-40976-6 |
| spellingShingle | M. Tovar Y. Moreno J. Sanz Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines Nature Communications |
| title | Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines |
| title_full | Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines |
| title_fullStr | Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines |
| title_full_unstemmed | Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines |
| title_short | Addressing mechanism bias in model-based impact forecasts of new tuberculosis vaccines |
| title_sort | addressing mechanism bias in model based impact forecasts of new tuberculosis vaccines |
| url | https://doi.org/10.1038/s41467-023-40976-6 |
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