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Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data

Little is known about the dynamics of the early stages of untreated active pulmonary tuberculosis: unknown are both the rates of progression and the model “scheme”. The “parallel” scheme assumes that infectiousness of tuberculosis cases is effectively predefined at the onset of the disease, and the...

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Autores principales: Avilov, Konstantin Konstantinovich, Romanyukha, Alexei Alexeevich, Belilovsky, Evgeny Mikhailovich, Borisov, Sergey Evgenevich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287187/
https://www.ncbi.nlm.nih.gov/pubmed/35891624
http://dx.doi.org/10.1016/j.idm.2022.06.007
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author Avilov, Konstantin Konstantinovich
Romanyukha, Alexei Alexeevich
Belilovsky, Evgeny Mikhailovich
Borisov, Sergey Evgenevich
author_facet Avilov, Konstantin Konstantinovich
Romanyukha, Alexei Alexeevich
Belilovsky, Evgeny Mikhailovich
Borisov, Sergey Evgenevich
author_sort Avilov, Konstantin Konstantinovich
collection PubMed
description Little is known about the dynamics of the early stages of untreated active pulmonary tuberculosis: unknown are both the rates of progression and the model “scheme”. The “parallel” scheme assumes that infectiousness of tuberculosis cases is effectively predefined at the onset of the disease, and the “serial” scheme considers all cases to be non-infectious at the onset, with some of them later becoming infectious. Our aim was to estimate the progression of the early stages of pulmonary tuberculosis using data from a present-day population. We used the routine notification data from Moscow, Russia, 2013–2018 that contained the results and time of the last fluorographic screening preceding the detection of tuberculosis cases. This provided time limits on the duration of untreated tuberculosis. Parameters of TB progression under both models were estimated. By the goodness of fit to the data, we could prefer neither the “parallel”, nor the “serial” model, although the latter had a bit worse fit. On the other hand, the observed rise in the fraction of infectious tuberculosis cases with the time since the last screening was explained by the “serial” model in a more plausible way – as gradual progression of some cases to infectiousness. The “parallel” model explained it through less realistic quick removal of non-infectious cases and accumulation of the infectious ones. The results demonstrate the potential of using such detection data enriched with reassessments of the previous screenings.
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spelling pubmed-92871872022-07-25 Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data Avilov, Konstantin Konstantinovich Romanyukha, Alexei Alexeevich Belilovsky, Evgeny Mikhailovich Borisov, Sergey Evgenevich Infect Dis Model Original Research Article Little is known about the dynamics of the early stages of untreated active pulmonary tuberculosis: unknown are both the rates of progression and the model “scheme”. The “parallel” scheme assumes that infectiousness of tuberculosis cases is effectively predefined at the onset of the disease, and the “serial” scheme considers all cases to be non-infectious at the onset, with some of them later becoming infectious. Our aim was to estimate the progression of the early stages of pulmonary tuberculosis using data from a present-day population. We used the routine notification data from Moscow, Russia, 2013–2018 that contained the results and time of the last fluorographic screening preceding the detection of tuberculosis cases. This provided time limits on the duration of untreated tuberculosis. Parameters of TB progression under both models were estimated. By the goodness of fit to the data, we could prefer neither the “parallel”, nor the “serial” model, although the latter had a bit worse fit. On the other hand, the observed rise in the fraction of infectious tuberculosis cases with the time since the last screening was explained by the “serial” model in a more plausible way – as gradual progression of some cases to infectiousness. The “parallel” model explained it through less realistic quick removal of non-infectious cases and accumulation of the infectious ones. The results demonstrate the potential of using such detection data enriched with reassessments of the previous screenings. KeAi Publishing 2022-06-30 /pmc/articles/PMC9287187/ /pubmed/35891624 http://dx.doi.org/10.1016/j.idm.2022.06.007 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research Article
Avilov, Konstantin Konstantinovich
Romanyukha, Alexei Alexeevich
Belilovsky, Evgeny Mikhailovich
Borisov, Sergey Evgenevich
Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title_full Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title_fullStr Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title_full_unstemmed Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title_short Mathematical modelling of the progression of active tuberculosis: Insights from fluorography data
title_sort mathematical modelling of the progression of active tuberculosis: insights from fluorography data
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287187/
https://www.ncbi.nlm.nih.gov/pubmed/35891624
http://dx.doi.org/10.1016/j.idm.2022.06.007
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