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Bayesian Analysis of Tuberculosis Spread Scenarios in Regions of Russian Federation Научная публикация

Журнал

Mathematics
, E-ISSN: 2227-7390

Вых. Данные

Год: 2026, Том: 14, Номер: 10, Номер статьи : 1600, Страниц : DOI: 10.3390/math14101600

Ключевые слова

mathematical modeling; tuberculosis; multidrug-resistant forms; inverse problem; Sobol analysis; optimization; Bayesian approach; probabilistic forecasting

Авторы

Krivorotko Olga ^1,2 , Neverov Andrei ¹ , Schwartz Yakov ³ , Kaminskiy Grigoriy ⁴ , Zyatkov Nikolay ¹ , Laushkina Zhanna ³

Организации

1	Research Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sirius, Russia
2	Sobolev Institute, Mathematics of SB RAS, 630090 Novosibirsk, Russia
3	Novosibirsk TB Research Institute, the Ministry of Health RF, 630040 Novosibirsk, Russia
4	Tula Regional Clinical Center for the Prevention and Control of AIDS and Infectious Diseases, 300002 Tula, Russia

Understanding the heterogeneous spread of tuberculosis (TB), particularly multidrug-resistant (MDR) forms and the role of subclinical infection, is critical for achieving the WHO End TB strategy. This study develops a novel compartmental model that explicitly incorporates incipient and subclinical TB together with MDR forms, and links them to case detection and treatment pathways. The key innovation lies in integrating a sensitivity-based identifiability analysis with a Bayesian MCMC framework to quantify parameter uncertainty and correlations directly from regional surveillance data. Applied to five high-burden regions of the Russian Federation (2009–2020), the approach reveals strong heterogeneity in epidemic drivers: wide credible intervals for contagiousness, the rate of progression to bacterio-positive (BE+) states, and detection rates. The probabilistic forecasts up to 2025 are validated against 2021–2023 data. The region-specific differences in these correlated parameters dictate transmission dynamics, and improving detection of BE+ cases is the most effective lever for control.

Krivorotko O. , Neverov A. , Schwartz Y. , Kaminskiy G. , Zyatkov N. , Laushkina Z.
Bayesian Analysis of Tuberculosis Spread Scenarios in Regions of Russian Federation
Mathematics. 2026. V.14. N10. 1600 . DOI: 10.3390/math14101600 WOS Scopus OpenAlex

Поступила в редакцию:	25 мар. 2026 г.
Принята к публикации:	6 мая 2026 г.
Опубликована online:	8 мая 2026 г.

≡ Web of science:	WOS:001774988600001
≡ Scopus:	2-s2.0-105040056694
≡ OpenAlex:	W7160828698