{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:49:10Z","timestamp":1773272950770,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Background: Tuberculosis remains a major global public health challenge. Mathematical models are essential for strategic planning and evaluation of tuberculosis control programs, while addressing socioeconomic risk factors has proven key to accelerating incidence declines. Therefore, this study quantitatively assesses the impact of socioeconomic interventions on tuberculosis incidence in Kazakhstan. Methods: A modified SIR compartmental model was developed in Python 3.12 to simulate tuberculosis transmission dynamics. Parameters were calibrated using the Nelder\u2013Mead simplex algorithm, and predictive performance was evaluated via hold-out validation. Scenario-based projections were generated to explore the impact of socioeconomic improvements on future tuberculosis incidence. Results: The calibrated SIR model demonstrated strong predictive accuracy, achieving a mean absolute percentage error of 2.3%. The sensitivity analysis revealed that the model is robust to moderate socioeconomic perturbations, with healthcare funding and unemployment rate as the primary uncertainty drivers. Scenario simulations showed that enhanced financial assistance for tuberculosis patients produced the largest effect beyond baseline. Optimization results indicate that 7.4% rise in GDP per capita, 10.2% increase in healthcare funding, 23.1% and 19.1% reductions in poverty and unemployment rates, and 40.2% growth in tuberculosis patient financial support relative to 2024 are sufficient to achieve the WHO\u2019s End TB Strategy 2030 target. Conclusions: The model offers a valuable tool for tuberculosis forecasting and intervention evaluation, highlighting the synergistic role of socioeconomic measures in achieving global elimination goals.<\/jats:p>","DOI":"10.3390\/ijerph23030351","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:00:37Z","timestamp":1773219637000},"page":"351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Socioeconomic Interventions for WHO\u2019s End TB Strategy Targets: Insights from SIR Modelling in Kazakhstan"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1438-2823","authenticated-orcid":false,"given":"Temirlan","family":"Ukubayev","sequence":"first","affiliation":[{"name":"Department of Informatics and Biostatistics, Karaganda Medical University, Karaganda 100000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8030-5407","authenticated-orcid":false,"given":"Berik","family":"Koichubekov","sequence":"additional","affiliation":[{"name":"Department of Informatics and Biostatistics, Karaganda Medical University, Karaganda 100000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina","family":"Sorokina","sequence":"additional","affiliation":[{"name":"Department of Informatics and Biostatistics, Karaganda Medical University, Karaganda 100000, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2374-5481","authenticated-orcid":false,"given":"Donatas","family":"Austys","sequence":"additional","affiliation":[{"name":"Department of Public Health, Institute of Health Sciences, Faculty of Medicine, Vilnius University, 03101 Vilnius, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024). Global Tuberculosis Report 2024, World Health Organization. [1st ed.]."},{"key":"ref_2","unstructured":"World Health Organization (2021). Global Tuberculosis Report 2021, World Health Organization. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e074208","DOI":"10.1136\/bmjopen-2023-074208","article-title":"Epidemiology of tuberculosis in Kazakhstan: Data from the Unified National Electronic Healthcare System 2014\u20132019","volume":"13","author":"Sakko","year":"2023","journal-title":"BMJ Open"},{"key":"ref_4","first-page":"59","article-title":"Analysis of the incidence of tuberculosis in the population of Akmola region before, during and a year after the COVID-19 pandemic (2019, 2020 and 2021)","volume":"4","author":"Tleubergenov","year":"2024","journal-title":"Med. Ecol."},{"key":"ref_5","first-page":"11","article-title":"Progress toward tuberculosis control and determinants of treatment outcomes\u2014Kazakhstan, 2000\u20132002","volume":"55","author":"Bumburidi","year":"2006","journal-title":"MMWR Suppl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gabdullina, M., Maes, E.F., Horth, R.Z., Dzhazybekova, P., Amanova, G.N., Zikriyarova, S., and Nabirova, D.A. (2023). COVID-19 pandemic and other factors associated with unfavorable tuberculosis treatment outcomes\u2014Almaty, Kazakhstan, 2018\u20132021. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1247661"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bekshin, Z., Askarov, A., Abduraimov, Y., Rsaliyev, A., Bissenova, G., Amirkhanova, N., Nurbekova, Z., and Temirbekova, A. (2025). Tuberculosis and Impact of COVID-19 on Spread of Epidemics in Kazakhstan. Pathogens, 14.","DOI":"10.3390\/pathogens14060559"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e07371","DOI":"10.1016\/j.heliyon.2021.e07371","article-title":"The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review","volume":"7","author":"Alfred","year":"2021","journal-title":"Heliyon"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Colijn, C., Cohen, T., and Murray, M. (2007). Mathematical Models of Tuberculosis: Accomplishments and Future Challenges. Proceedings of the BIOMAT 2006, Manaus, Brazil, 27\u201330 November 2006, WORLD SCIENTIFIC.","DOI":"10.1142\/9789812708779_0008"},{"key":"ref_10","first-page":"700","article-title":"A contribution to the mathematical theory of epidemics","volume":"115","author":"Kermack","year":"1927","journal-title":"Proc. R. Soc. Lond. Ser. A Contain. Pap. Math. Phys. Character"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1590\/S1135-57272009000500010","article-title":"Modelos matem\u00e1ticos utilizados en el estudio de las enfermedades transmisibles","volume":"83","author":"Casals","year":"2009","journal-title":"Rev. Esp. Salud Publica"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ochieng, F.O. (2025). SEIRS model for TB transmission dynamics incorporating the environment and optimal control. BMC Infect. Dis., 25.","DOI":"10.1186\/s12879-025-10710-2"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"(2017). Mixed vaccination strategy for the control of tuberculosis: A case study in China. Math. Biosci. Eng., 14, 695\u2013708.","DOI":"10.3934\/mbe.2017039"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Saini, P., and Bhattacharyya, S. (2025). Impact of multiple consultations and switching in treatment-seeking in tuberculosis: Mathematical modelling and optimal control. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0324330"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Getchell, M., Ansah, J.P., Lim, D., Basilio, R., Tablizo, F., Mahasirimongkol, S., Sawaengdee, W., and Matchar, D. (2024). Dynamic modelling of improved diagnostic testing for drug-resistant tuberculosis in high burden settings. BMC Infect. Dis., 24.","DOI":"10.1186\/s12879-024-10027-6"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kendall, E.A., Shrestha, S., Cohen, T., Nuermberger, E., Dooley, K.E., Gonzalez-Angulo, L., Churchyard, G.J., Nahid, P., Rich, M.L., and Bansbach, C. (2017). Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. PLoS Med., 14.","DOI":"10.1371\/journal.pmed.1002202"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fofana, M.O., Knight, G.M., Gomez, G.B., White, R.G., and Dowdy, D.W. (2014). Population-Level Impact of Shorter-Course Regimens for Tuberculosis: A Model-Based Analysis. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0096389"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3634","DOI":"10.1038\/s41598-022-07536-2","article-title":"Mathematical analysis of a two-strain tuberculosis model in Bangladesh","volume":"12","author":"Kuddus","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kuddus, M.A., Meehan, M.T., White, L.J., McBryde, E.S., and Adekunle, A.I. (2020). Modeling drug-resistant tuberculosis amplification rates and intervention strategies in Bangladesh. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236112"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Li, M., and Yuan, S. (2017). Analysis of Transmission and Control of Tuberculosis in Mainland China, 2005\u20132016, Based on the Age-Structure Mathematical Model. Int. J. Environ. Res. Public Health, 14.","DOI":"10.3390\/ijerph14101192"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11538-022-01019-1","article-title":"Evaluating Strategies for Tuberculosis to Achieve the Goals of WHO in China: A Seasonal Age-Structured Model Study","volume":"84","author":"Xue","year":"2022","journal-title":"Bull. Math. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2323724","DOI":"10.1080\/17513758.2024.2323724","article-title":"Global dynamics of discrete mathematical models of tuberculosis","volume":"18","author":"Elaydi","year":"2024","journal-title":"J. Biol. Dyn."},{"key":"ref_23","first-page":"458","article-title":"Studying the efficacy of isolation as a control strategy and elimination of tuberculosis in India: A mathematical model","volume":"8","author":"Bhadauria","year":"2023","journal-title":"Infect. Dis. Model."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiang, H., Li, R., Zhang, T., Wang, Q., Liu, H., Hua, Z., Zhang, S., Zheng, X., Zhuang, G., and Shen, M. (2025). Cost-effectiveness of preventive treatment regimens for latent tuberculosis infection among key community-level populations. BMC Med., 23.","DOI":"10.1186\/s12916-025-04413-3"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gallo, L.G., Oliveira, A.F.D.M., Abrah\u00e3o, A.A., Sandoval, L.A.M., Martins, Y.R.A., Almir\u00f3n, M., Dos Santos, F.S.G., Ara\u00fajo, W.N., De Oliveira, M.R.F., and Peixoto, H.M. (2020). Ten Epidemiological Parameters of COVID-19: Use of Rapid Literature Review to Inform Predictive Models During the Pandemic. Front. Public Health, 8.","DOI":"10.3389\/fpubh.2020.598547"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.3201\/eid2611.201074","article-title":"Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19","volume":"26","author":"Biggerstaff","year":"2020","journal-title":"Emerg. Infect. Dis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, S., Wang, H., and Hu, Y. (2025). Analysis of drug-resistant tuberculosis transmission dynamics in China using fractional stochastic model. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0335889"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"24824","DOI":"10.1038\/s41598-024-76721-2","article-title":"Modeling tuberculosis transmission dynamics in Kazakhstan using SARIMA and SIR models","volume":"14","author":"Kalizhanova","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/13873954.2021.1881560","article-title":"Analysing of Tuberculosis in Turkey through SIR, SEIR and BSEIR Mathematical Models","volume":"27","author":"Ucakan","year":"2021","journal-title":"Math. Comput. Model. Dyn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2394665","DOI":"10.1080\/17513758.2024.2394665","article-title":"The dynamics of tuberculosis transmission model with different genders","volume":"18","author":"Wang","year":"2024","journal-title":"J. Biol. Dyn."},{"key":"ref_31","first-page":"288","article-title":"Reproduction numbers of infectious disease models","volume":"2","year":"2017","journal-title":"Infect. Dis. Model."},{"key":"ref_32","unstructured":"World Health Organization (2023). Global Tuberculosis Report 2023, World Health Organization. Available online: https:\/\/www.who.int\/teams\/global-tuberculosis-programme\/tb-reports\/global-tuberculosis-report-2023."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2240","DOI":"10.1016\/j.socscimed.2009.03.041","article-title":"Drivers of tuberculosis epidemics: The role of risk factors and social determinants","volume":"68","author":"Jaramillo","year":"2009","journal-title":"Soc. Sci. Med."},{"key":"ref_34","unstructured":"World Health Organization (2022). Global Tuberculosis Report 2022, World Health Organization. [1st ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110858","DOI":"10.1016\/j.compbiomed.2025.110858","article-title":"Mathematical model for analysing the interplay between income, nutrition, and tuberculosis dynamics","volume":"196","author":"Yadav","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_36","first-page":"1037","article-title":"Modelling the potential impact of TB-funded prevention programs on the transmission dynamics of TB","volume":"10","author":"Mbalilo","year":"2025","journal-title":"Infect. Dis. Model."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"375","DOI":"10.5588\/ijtld.14.0423","article-title":"The epidemiological advantage of preferential targeting of tuberculosis control at the poor","volume":"19","author":"Andrews","year":"2015","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pando, C., Hazel, A., Tsang, L.Y., Razafindrina, K., Andriamiadanarivo, A., Rabetombosoa, R.M., Ambinintsoa, I., Sadananda, G., Small, P.M., and Knoblauch, A.M. (2023). A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar. BMC Public Health, 23.","DOI":"10.1186\/s12889-023-16425-w"},{"key":"ref_39","unstructured":"(2025, December 24). Bureau of National Statistics Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, Available online: https:\/\/stat.gov.kz\/en\/."},{"key":"ref_40","unstructured":"(2025, December 24). National Scientific Center of Phthisiopulmonology. Available online: https:\/\/www.nncf.kz."},{"key":"ref_41","unstructured":"(2025, December 24). Ministry of National Economy of the Republic of Kazakhstan, Available online: https:\/\/www.gov.kz\/memleket\/entities\/economy?lang=en."}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/3\/351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:05:40Z","timestamp":1773219940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/23\/3\/351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["ijerph23030351"],"URL":"https:\/\/doi.org\/10.3390\/ijerph23030351","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]}}}