Evidence map›Paper›PMID 35891624›Full record

ArticleInfectious Disease Modelling2022

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

Konstantin Konstantinovich Avilov, Alexei Alexeevich Romanyukha, Evgeny Mikhailovich Belilovsky, Sergey Evgenevich Borisov

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Article in Infectious Disease Modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Konstantin Konstantinovich AvilovInstitute for Numerical Mathematics, Russian Academy of Sciences, 8, Gubkina Str., Moscow, 119333, Russia.
Alexei Alexeevich RomanyukhaInstitute for Numerical Mathematics, Russian Academy of Sciences, 8, Gubkina Str., Moscow, 119333, Russia.
Evgeny Mikhailovich BelilovskyMoscow Research and Clinical Center for Tuberculosis Control of the Moscow Government Department of Health, 10, Stromynka Str., Moscow, 107076, Russia.
Sergey Evgenevich BorisovMoscow Research and Clinical Center for Tuberculosis Control of the Moscow Government Department of Health, 10, Stromynka Str., Moscow, 107076, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Fluorographic screening dataMathematical modelingMinimal tuberculosisModel structureNatural history of tuberculosis (TB)

Identifiers

PMID35891624
PMCPMC9287187

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