1. World Health Organization. The End TB Strategy. Geneva: World Health Organization; 2015.

  2. World Health Organization. Global Tuberculosis Report 2022.Geneva: World Health Organization; 2022.

  3. Kementerian Kesehatan Republik Indonesia. Strategi Nasional Penanggulangan Tuberkulosis di Indonesia 2020–2024. Jakarta: Kementerian Kesehatan Republik Indonesia; 2020.

  4. Rahayu SR, Katsuyama H, Demura M, et al. Factors associated with tuberculosis cases in Semarang District, Indonesia: Case-control study performed in the area where case detection rate was extremely low. Environ Health Prev Med. 2015; 20 (4): 253–261.

DOI: 10.1007/s12199-015-0443-9

  5. Zhang Y, Liu M, Wu SS, et al. Spatial distribution of tuberculosis and its association with meteorological factors in mainland China. BMC Infect Dis. 2019; 19 (1): 379. DOI: 10.1186/s12879-019-4008-1

  6. Beiranvand R, Karimi A, Delpisheh A, et al. Correlation Assessment of Climate and Geographic Distribution of Tuberculosis Using Geographical Information System (GIS). Iran J Public Health. 2016; 45 (1): 86–93.

  7. The Republic of Indonesia Joint External Monitoring Mission for Tuberculosis. Geneva: World Health Organization; 2020.

  8. Balakrishnan SK, Suseela RP, Mrithyunjayan S, et al. Individuals’ Vulnerability Based Active Surveillance for TB: Experiences from India. Trop Med Infect Dis. 2022; 7 (12): 441.

DOI: 10.3390/tropicalmed7120441

  9. Vanleeuw L, Zembe-Mkabile W, Atkins S. Falling through the cracks: Increased vulnerability and limited social assistance for TB patients and their households during COVID-19 in Cape Town, South Africa. PLoS Glob Public Health. 2022; 2 (7): e0000708. DOI: 10.1371/journal.pgph.0000708

  10.Trivianita N, Marsisno W, Wilantika N. Social Vulnerability Index to Tuberculosis of Provinces in Indonesia. Adv Soc Sci Educ Hum Res. Atlantis Press. 2019; 216: 170–180.

  11. Handayani S, Hinchcliff R, Hasibuan ZA. Development of Tuberculosis Vulnerability Assessment Conceptual Framework Using Automatic Content Analysis. Healthcare Transformation with Informatics and Artificial Intelligence. Amsterdam: IOS Press; 2023. p. 220–223. DOI: 10.3233/SHTI230467

  12. Ward V, West R, Smith S, et al. The role of informal networks in creating knowledge among healthcare managers: A prospective case study. Health Serv Deliv Res. 2014; 2 (12). DOI: 10.3310/hsdr02120

  13. Engstrom T, Strong J, Sullivan C, et al. A Comparison of LeximancerSemi-automated Content Analysis to Manual Content Analysis: A Healthcare Exemplar Using Emotive Transcripts of COVID-19 Hospital Staff Interactive Webcasts. Int J Qual Methods 2022; 21:16094069221118993. DOI: 10.1177/16094069221118993

  14. Rahmah A, Santoso HB, Hasibuan ZA. Critical Review of Technology-Enhanced Learning using Automatic Content Analysis Case Study of TEL Maturity Assessment Formulation. Int J Adv Comput Sci Appl. 2022; 13 (1). DOI: 10.14569/IJACSA.2022.0130148

  15. Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Inf Sci Syst. 2014; 2: 3.

DOI: 10.1186/2047-2501-2-3

  16. Yunita A, Santoso HB, Hasibuan ZA. ‘Everything is data’: Towards one big data ecosystem using multiple sources of data on higher education in Indonesia. J Big Data. 2022; 9: 91.

DOI: 10.1186/s40537-022-00639-7

  17. Zhou X, Lee EWJ, Wang X, et al. Infectious diseases prevention and control using an integrated health big data system in China. BMC Infect Dis. 2022; 22: 344. DOI: 10.1186/s12879-022-07316-3

  18. Bhardwaj N, Wodajo B, Spano A, et al. The Impact of Big Data on Chronic Disease Management. Health Care Manag. 2018; 37 (1): 90–98. DOI: 10.1097/HCM.0000000000000194

  19. Bhargava S, Jain M, Jain M. In tuberculosis, “One size does not fit all.” Lung India. 2019; 36 (1): 1–2. DOI: 10.4103/lungindia.lungindia_489_18