A Stochastic Projection for Tuberculosis Elimination in Indonesia by 2030

  • Novi Reandy Sasmita Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • Maya Ramadani Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • Muhammad Ikhwan Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • Munawwarah Munawwarah Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • Latifah Rahayu Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • Selvi Mardalena Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
  • M. Ischaq Nabil Asshiddiqi School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
  • Suyanto Suyanto Department of Public Health and Community Medicine, Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia
  • Nanda Safira Department of Epidemiology, Faculty of Medicine, Prince of Songkla University, Hatyai, Thailand
DOI: https://doi.org/10.56338/mppki.v8i11.8548
Keywords: Tuberculosis, SStochastic Epidemic Model, Gaussian Noise, Indonesia

Abstract

Introduction: Indonesia, with the world's second-highest tuberculosis (TB) burden, has targeted TB elimination (65 cases per 100,000) by 2030. This study aimed to evaluate the feasibility of achieving this goal by projecting TB incidence trends using a stochastic epidemic model that accounts for the uncertainties inherent in TB transmission dynamics in latent TB infections.

Methods: The initial values for state variables and parameters were derived from a comprehensive literature review and calibrated against publicly available epidemiological data from the Indonesian Ministry of Health reports from 2018-2022. A Susceptible, Vaccinated, Three Exposed, Three Infectious, Recovered (SVE3I3R) model was developed, incorporating Gaussian noise into the exposed compartments to simulate real-world unpredictability in latent infection dynamics. The model was solved numerically using the fourth-order Runge-Kutta (RK4) method in R software. Key outcomes measured were the projected incidence of drug-susceptible TB (DS-TB), multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB).

Results: Model projections suggest that the overall TB incidence rate will fall from 387 cases per 100,000 people in 2023 to a projected 320 cases per 100,000 by 2030. However, this remains far above the national target. While DS-TB cases decreased to 730,283, MDR-TB and XDR-TB cases were projected to surge dramatically to 120,939 cases and 104,651 individuals, respectively. The estimation signals a critical shift in the epidemic's profile.

Conclusions: Indonesia is not on track to achieve its 2030 TB elimination target under current interventions. The alarming rise of drug-resistant TB necessitates an urgent, aggressive, and multifaceted policy response. This study underscores the critical value of incorporating stochasticity into epidemiological models for more realistic forecasting and public health planning in high-burden settings.

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References

  1. Kim TH, Krichen M, Ojo S, Alamro MA, Sampedro GA. TSSG-CNN: A tuberculosis semantic segmentation-guided model for detecting and diagnosis using the adaptive convolutional neural network. Diagnostics. 2024 Jun;14(11):1174–93.
  2. Aryawati W, Suharman S, Herlinda E, Putra AM, Siregar FE. Pencegahan penularan Tuberkulosis (TB) melalui kegiatan skrining dan edukasi kepada penghuni lembaga permasyarakatan kelas II A Metro. J Kreat Pengabdi Kpd Masy. 2023 May;6(5):2040–8.
  3. WHO. Global tuberculosis report 2024 [Internet]. Genewa; 2024. Available from: https://iris.who.int/server/api/core/bitstreams/7292c91e-ffb0-4cef-ac39-0200f06961ea/content
  4. Saputra A, Sofyan H, Kesuma ZM, Sasmita NR, Wichaidit W, Chongsuvivatwong V. Spatial patterns of tuberculosis in Aceh Province during the COVID-19 pandemic: a geospatial autocorrelation assessment. IOP Conf Ser Earth Environ Sci [Internet]. 2024 Jun 1;1356(1):012099. Available from: https://iopscience.iop.org/article/10.1088/1755-1315/1356/1/012099
  5. World Health Organization. Tuberculosis profile: Indonesia [Internet]. WHO. 2024 [cited 2025 Sep 6]. p. 1. Available from: https://worldhealthorg.shinyapps.io/tb_profiles/?_inputs_&tab=%22charts%22&lan=%22EN%22&iso2=%22AF%22&entity_type=%22country%22
  6. Meiyanti M, Bachtiar A, Kusumaratna RK, Alfiyyah A, Machrumnizar M, Pusparini P. Tuberculosis treatment outcomes and associated factors: A retrospective study in West Nusa Tenggara, Indonesia. Narra J [Internet]. 2024 Dec 18;4(3):1–11. Available from: https://narraj.org/main/article/view/1660
  7. Sasmita NR, Khairul M, Fikri MK, Rahayu L, Kesuma ZM, Mardalena S, et al. Relative Risk and Distribution Assessment of Tuberculosis Cases: A Time-Series Ecological Study in Aceh, Indonesia. Media Publ Promosi Kesehat Indones [Internet]. 2025 Jun 5;8(6):407–17. Available from: https://jurnal.unismuhpalu.ac.id/index.php/MPPKI/article/view/7264
  8. President of the Republic of Indonesia. Presidential Regulation Number 67 of 2021 concerning Tuberculosis Control. Republic of Indonesia, Number 67 of 2021 Indonesia; 2021 p. 1–107.
  9. Sofonea MT, Cauchemez S, Boëlle P-Y. Epidemic models: why and how to use them. Anaesth Crit Care Pain Med [Internet]. 2022 Apr;41(2):1–3. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352556822000297
  10. Mettle FO, Osei Affi P, Twumasi C. Modelling the Transmission Dynamics of Tuberculosis in the Ashanti Region of Ghana. Interdiscip Perspect Infect Dis [Internet]. 2020 Mar 31;2020(1):1–16. Available from: https://www.hindawi.com/journals/ipid/2020/4513854/
  11. Liu S, Bi Y, Liu Y. Modeling and dynamic analysis of tuberculosis in mainland China from 1998 to 2017: the effect of DOTS strategy and further control. Theor Biol Med Model [Internet]. 2020 Dec 4;17(1):1–6. Available from: https://tbiomed.biomedcentral.com/articles/10.1186/s12976-020-00124-9
  12. Ullah S, Khan MA, Farooq M, Gul T. Modeling and analysis of Tuberculosis (TB) in Khyber Pakhtunkhwa, Pakistan. Math Comput Simul [Internet]. 2019 Nov;165(1):181–99. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378475419301089
  13. Mahardika D, Kartika S. Dynamic system of tuberculosis model using optimal control in Semarang City Indonesia. BAREKENG J Ilmu Mat dan Terap. 2024 Mar;18(1):43–52.
  14. Sasmita NR, Geater AF, Kammanee A, Chongsuvivatwong V. Is The Recovery Rate In Latent Tuberculosis Infection Significant In Reducing Tuberculosis Transmission In Indonesia?: A Mathematical Model Study In Epidemiology. In: RSU Research Conference 2019. Pathum Thani, Thailand: Rangsit University; 2019. p. 101–11.
  15. Sulayman F, Abdullah FA, Mohd MH. An SVEIRE model of tuberculosis to assess the effect of an imperfect vaccine and other exogenous factors. Mathematics. 2021 Feb;9(4):327–50.
  16. Nivetha S, Das P, Ghosh M. A comparison of stochastic and deterministic dynamics of tuberculosis model. Stoch Anal Appl [Internet]. 2024 Nov 13;42(6):1085–109. Available from: https://www.tandfonline.com/doi/full/10.1080/07362994.2024.2413632
  17. Saha P, Kumar Pal K, Ghosh U, Kumar Tiwari P. Dynamic analysis of deterministic and stochastic SEIR models incorporating the Ornstein–Uhlenbeck process. Chaos An Interdiscip J Nonlinear Sci [Internet]. 2025 Feb 1;35(2). Available from: https://pubs.aip.org/cha/article/35/2/023165/3337401/Dynamic-analysis-of-deterministic-and-stochastic
  18. Zhang Y, Li Y. Evolutionary Dynamics of Stochastic SEIR Models with Migration and Human Awareness in Complex Networks. Complexity [Internet]. 2020 May 28;2020(1):1–15. Available from: https://www.hindawi.com/journals/complexity/2020/3768083/
  19. Caren GJ, Iskandar D, Pitaloka DA, Abdulah R, Suwantika AA. Covid-19 pandemic disruption on the management of tuberculosis treatment in Indonesia. J Multidiscip Healthc. 2022 Jan;15(1):175–83.
  20. Zhou H, Ren H, Royer P, Hou H, Yu X-Y. Big Data Analytics for Long-Term Meteorological Observations at Hanford Site. Atmosphere (Basel) [Internet]. 2022 Jan 14;13(1):1–17. Available from: https://www.mdpi.com/2073-4433/13/1/136
  21. Reskiaddin LO, Ahsan A, Fitri A, Hubaybah H, Putri FE, Sasmita NR. Evaluating the Impact of Smoke-Free Policies in Jambi, Indonesia: A Mixed-Methods Approach. Asian Pacific J Cancer Prev [Internet]. 2025 May 1;26(5):1815–21. Available from: https://journal.waocp.org/article_91670.html
  22. Sasmita NR, Phonna RA, Kesuma ZM, Kamal S, Yusya N. Spatial-Temporal Epidemiology of COVID-19 in Aceh, Indonesia: A Statistical Perspective. Unnes J Public Heal. 2024;3(2):67–79.
  23. Chopra KK, Pandey P, Malik A, Indora A, Pandey S. Infection control and preventing the transmission of tuberculosis in high-risk centres - recovery shelter for homeless people. Indian J Tuberc [Internet]. 2023 Apr;70(2):158–61. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0019570722001834
  24. Sasmita NR, Arifin M, Kesuma ZM, Rahayu L, Mardalena S, Kruba R. Spatial Estimation for Tuberculosis Relative Risk in Aceh Province, Indonesia: A Bayesian Conditional Autoregressive Approach with the Besag-York-Mollie (BYM) Model. J Appl Data Sci [Internet]. 2024 May 15;5(2):342–56. Available from: https://bright-journal.org/Journal/index.php/JADS/article/view/185
  25. Ma Z, Duan S, Wang W, Liu R, Li S, Shang Y, et al. Surveillance of close contacts of patients with infectious tuberculosis: a prospective cohort study. Antimicrob Resist Infect Control [Internet]. 2024 Jun 9;13(1):1–8. Available from: https://aricjournal.biomedcentral.com/articles/10.1186/s13756-024-01419-z
  26. Sikandar M, Xing L. Advances in the mechanisms of drug resistance of Mycobacterium tuberculosis. Microb Pathog [Internet]. 2025 Sep;206(1):1–18. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0882401025005868
  27. Chin KL, Anibarro L, Chang ZY, Palasuberniam P, Mustapha ZA, Sarmiento ME, et al. Impacts of MDR/XDR-TB on the global tuberculosis epidemic: Challenges and opportunities. Curr Res Microb Sci [Internet]. 2024;7(1):1–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2666517424000786
  28. Ministry of Health Indonesia. Indonesia Health Profile 2023 [Internet]. Ministry of Health Indonesia. Jakarta; 2024. Available from: https://kemkes.go.id/app_asset/file_content_download/172231123666a86244b83fd8.51637104.pdf
  29. Lye Koh H, Naim Abdul Kadir M, M. Noordin N, Yean Teh S. Tuberculosis Elimination in Malaysia by 2035: Linkages and Implications to SDGs. Int J Soc Sci Humanit. 2019 Nov;9(4):126–32.
  30. Kementrian Kesehatan. Profil Kesehatan Indonesia 2023. Jakarta: Kementerian Kesehatan Indonesia; 2024. 100 p.
  31. BPS-Statistics Indonesia. Mortality in Indonesia Results of the 2020 Long Form Population Census (in bahasa) [Internet]. BPS-Statistics Indonesia. Jakarta: BPS-Statistics Indonesia; 2020. 1–98 p. Available from: https://indonesia.unfpa.org/sites/default/files/pub-pdf/mortalitas-di-indonesia-hasil-long-form-sensus-penduduk-2020.pdf
  32. Widyaningsih P, Yumaroh SR, Saputro DRS. Spreading pattern of infectious diseases: susceptible, infected, recovered model with vaccination and drug-resistant cases (application on TB data in Indonesia). BAREKENG J Ilmu Mat dan Terap. 2024 Mar;18(1):0467–74.
  33. Moghaddam HT, Moghadam ZE, Khademi G, Bahreini A, Saeidi M. Tuberculosis: past, present and future. Int J Pediatr [Internet]. 2016;4(125):1243–55. Available from: http://
  34. Ojo MM, Peter OJ, Goufo EFD, Panigoro HS, Oguntolu FA. Mathematical model for control of tuberculosis epidemiology. J Appl Math Comput. 2023 Feb;69(1):69–87.
  35. Dheda K, Gumbo T, Maartens G, Dooley KE, McNerney R, Murray M, et al. The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir Med. 2017 Apr;5(4):291–360.
  36. Soodejani MT, Kazemi M, Moradi B, Mahmudimanesh M. Extensively Drug-resistant Tuberculosis in the World: From 1990 to 2019, by WHO Regions. Mediterr J Infect Microbes Antimicrob. 2024 Aug;18(1):16–24.
  37. Arinaminpathy N, Mukadi YD, Bloom A, Vincent C, Ahmedov S. Meeting the 2030 END TB goals in the wake of COVID-19: A modelling study of countries in the USAID TB portfolio. Yuan H-Y, editor. PLOS Glob Public Heal [Internet]. 2023 Oct 23;3(10):1–13. Available from: https://dx.plos.org/10.1371/journal.pgph.0001271
  38. Kuddus MA, Tithi SK, Theparod T. Modelling the Impact of Vaccination and Other Intervention Strategies on Asymptomatic and Symptomatic Tuberculosis Transmission and Control in Thailand. Vaccines [Internet]. 2025 Aug 15;13(8):1–20. Available from: https://www.mdpi.com/2076-393X/13/8/868
  39. Zhao K, Liu Z, Guo C, Xiang H, Liu L, Wang L. Modeling and analysis of transmission dynamics of tuberculosis with preventive treatment and vaccination strategies in China. Appl Math Model [Internet]. 2025 Feb;138(1):1–37. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0307904X24005328
  40. Shah H, Patel J, Rai S, Sen A. Advancing tuberculosis elimination in India: A qualitative review of current strategies and areas for improvement in tuberculosis preventive treatment. IJID Reg [Internet]. 2025 Mar;14(1):1–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S277270762400225X
  41. Pradipta IS, Idrus LR, Probandari A, Puspitasari IM, Santoso P, Alffenaar J-WC, et al. Barriers to Optimal Tuberculosis Treatment Services at Community Health Centers: A Qualitative Study From a High Prevalent Tuberculosis Country. Front Pharmacol [Internet]. 2022 Mar 25;13(1):1–12. Available from: https://www.frontiersin.org/articles/10.3389/fphar.2022.857783/full
  42. Bui VL, Hughes AE, Ragonnet R, Meehan MT, Henderson A, McBryde ES, et al. Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review. BMC Infect Dis [Internet]. 2024 Dec 6;24(1):1–11. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-024-10245-y
  43. Zhang X, Wang J, Yu C, Fei J, Luo T, Cao Z. Agent-Based Modeling of Epidemics: Approaches, Applications, and Future Directions. Technologies [Internet]. 2025 Jun 26;13(7):1–23. Available from: https://www.mdpi.com/2227-7080/13/7/272
  44. Karmakar M, Ragonnet R, Ascher DB, Trauer JM, Denholm JT. Estimating tuberculosis drug resistance amplification rates in high-burden settings. BMC Infect Dis [Internet]. 2022 Jan 24;22(1):1–12. Available from: https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07067-1
  45. Sanchini A, Lanni A, Giannoni F, Mustazzolu A. Exploring diagnostic methods for drug-resistant tuberculosis: A comprehensive overview. Tuberculosis [Internet]. 2024 Sep;148(1):1–17. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1472979224000489
  46. Jeon D, Kang H, Kwon Y-S, Yim J-J, Shim TS. Impact of Molecular Drug Susceptibility Testing on the Time to Multidrug-resistant Tuberculosis Treatment Initiation. J Korean Med Sci [Internet]. 2020;35(1):1–10. Available from: https://jkms.org/DOIx.php?id=10.3346/jkms.2020.35.e284
  47. Grigoryan Z, McPherson R, Harutyunyan T, Truzyan N, Sahakyan S. Factors Influencing Treatment Adherence Among Drug-Sensitive Tuberculosis (DS-TB) Patients in Armenia: A Qualitative Study. Patient Prefer Adherence [Internet]. 2022 Sep;16(1):2399–408. Available from: https://www.dovepress.com/factors-influencing-treatment-adherence-among-drug-sensitive-tuberculo-peer-reviewed-fulltext-article-PPA
  48. Brüggemann R, Schlumberger F, Chinshailo F, Willis M, Kadyrov A, Kalmambetova G, et al. Stigmatization and discrimination of female tuberculosis patients in Kyrgyzstan – a phenomenological study. Int J Equity Health [Internet]. 2025 Jul 1;24(1):1–14. Available from: https://equityhealthj.biomedcentral.com/articles/10.1186/s12939-025-02566-4
  49. Jiang W, Dong D, Febriani E, Adeyi O, Fuady A, Surendran S, et al. Policy gaps in addressing market failures and intervention misalignments in tuberculosis control: prospects for improvement in China, India, and Indonesia. Lancet Reg Heal - West Pacific [Internet]. 2024 May;46:101045. Available from: https://doi.org/10.1016/j.lanwpc.2024.101045
  50. Wells WA, Waseem S, Scheening S. The intersection of TB and health financing: defining needs and opportunities. IJTLD OPEN [Internet]. 2024 Sep 1;1(9):375–83. Available from: https://www.ingentaconnect.com/content/10.5588/ijtldopen.24.0324
  51. Gulumbe BH, Abdulrahim A, Ahmad SK, Lawan KA, Danlami MB. WHO report signals tuberculosis resurgence: Addressing systemic failures and revamping control strategies. Decod Infect Transm [Internet]. 2025;3:1–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2949924025000059
  52. Cobelens F, Suri RK, Helinski M, Makanga M, Weinberg AL, Schaffmeister B, et al. Accelerating research and development of new vaccines against tuberculosis: a global roadmap. Lancet Infect Dis [Internet]. 2022 Apr;22(4):e108–20. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1473309921008100
  53. Alfaqeeh M, Ewart S, Tanoto R, Buenastuti W, Isturini IA, Yosephine P, et al. New adult and adolescent tuberculosis vaccines and Indonesia: policy planning and evidence, November 2024. Vaccine [Internet]. 2025 Aug;62(1):1–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0264410X2500787X
  54. Saunders MJ, Montoya R, Quevedo L, Ramos E, Datta S, Evans CA. The social determinants of tuberculosis: a case-control study characterising pathways to equitable intervention in Peru. Infect Dis Poverty [Internet]. 2025 Jun 20;14(1):1–53. Available from: https://idpjournal.biomedcentral.com/articles/10.1186/s40249-025-01324-6
  55. Silva RD da, Farias ERG de, Graça JMB da, Pinheiro EMN, Cavalcante EF de O. Approaches and results of intersectoral actions for tuberculosis control in the world: A scoping review. PLoS One [Internet]. 2025 Jun 26;20(6):1–17. Available from: https://dx.plos.org/10.1371/journal.pone.0326784
Published
2025-11-01
How to Cite
Sasmita, N. R., Ramadani, M., Ikhwan, M., Munawwarah, M., Rahayu, L., Mardalena, S., Ischaq Nabil Asshiddiqi, M., Suyanto, S., & Safira, N. (2025). A Stochastic Projection for Tuberculosis Elimination in Indonesia by 2030 . Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 8(11), 1297-1314. https://doi.org/10.56338/mppki.v8i11.8548
Issue
Vol. 8 No. 11 (2025)
Section
Article

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