Open Access
MATEC Web Conf.
Volume 266, 2019
International Conference on Built Environment and Engineering 2018 - “Enhancing Construction Industry Through IR4.0” (IConBEE2018)
Article Number 02007
Number of page(s) 6
Section Environmental Sciences and Engineering (ESE)
Published online 20 February 2019
  1. N. Nishikiori. Tuberculosis control among high risk and vulnerable populations. [Online]. Retrieved from (2011) [Google Scholar]
  2. M. V. Burgos and A. S. Pym, Molecular epidemiology of tuberculosis. doi:10.1183/09031936.02.00400702, "European Respiratory Journal, doi: 10.1183/09031936.02.00400702, 20, 54S-65s (2002). [CrossRef] [Google Scholar]
  3. B. Mathema, N. E. Kurepina, P. J. Bifani, and B. N. Kreiswirth, Molecular epidemiology of tuberculosis: current insights. Clinical Microbiology Reviews, vol. 19 (4), no. doi: 10.1128/CMR.00061-05, 658-85 (2006). [CrossRef] [Google Scholar]
  4. S. Narayanan, Molecular epidemiology of tuberculosis, Indian J Med Res, 233-247 (2004). [Google Scholar]
  5. A. R. Abdul Rasam, N. M. Shariff, J. F. Dony, and A. Misni, Socio-environmental factors and tuberculosis: an exploratory spatial analysis in Peninsular Malaysia, International Journal of Engineering & Technology, 7 (3.11), 187-192, (2018). [CrossRef] [Google Scholar]
  6. S. Azhar Shah, M. I. Mohd Nor, A. H. Harman Shah, and T. Aris, Penggunaan Aplikasi GIS dalam Penyakit Tuberkulosis di Cheras, Kuala Lumpur, Malaysia., Malaysian Journal Public Health Medicine, 2, 15-26, (2002). [Google Scholar]
  7. A. R. Abdul Rasam, N. M. Shariff, and J. F. Dony, Identifying High-Risk Populations of Tuberculosis Using Environmental Factors and GIS Based Multi- Criteria Decision Making Method., ISPRS - International Archives of the Photogrammetry, Remote Sensing International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W1, 9-13, (2016). [CrossRef] [Google Scholar]
  8. D. O. Fuller, A. Troyo, T. O. Alimi, and J. C. Beier, Participatory Risk Mapping of Malaria Vector Exposure in Northern South America using Environmental and Population Data, no. doi: 10.1016/j.apgeo, 48, 1-7, (2014). [Google Scholar]
  9. D. J. Daley and J. Gani,Epidemic Modeling: An Introduction. NY: Cambridge Uniersity Press., (2005). [Google Scholar]
  10. H. Lin,Use of Spatial Information to Predict Multidrug Resistance in. Emerging Infectious Diseases, 18, 5–7, (2012). [Google Scholar]
  11. K. Middelkoop, L. Bekker, C Morrow, N. Lee, and R. Wood, Decreasing household contribution to TB transmission with age: a retrospective geographic analysis of young people in a South African township. BMC Infectious Diseases, 14, 221, 1-7, (2014). [CrossRef] [Google Scholar]
  12. P. K. Moonan, What Is the Outcome of Targeted Tuberculosis Screening Based on Universal Genotyping and Location ?, Am J Respir Crit Care Med, 174, 599–604, (2006). [CrossRef] [Google Scholar]
  13. E. Musenge, P. Vounatsou, M. Collinson, S. Tollman, and K. Kahn, The contribution of spatial analysis to unders.anding HIV/TB mortality in children: a structural equation modelling approach., Glob Health Action, (2013). [Google Scholar]
  14. WHO, World Health Organization Web site. [Online]. [Google Scholar]
  15. R. S. Ostfeld, G. E. Glass, and F. Keesing, Spatial epidemiology: an emerging (or re-emerging) discipline, Trends in Ecology & Evolution, 20, 6, 328-36, (2005). [CrossRef] [PubMed] [Google Scholar]
  16. B. Mathema, N. E. Kurepina, P. J. Bifani, and B. N. Kreiswirth, Molecular epidemiology of tuberculosis:current insights. Clinical Microbiology Reviews, 19, 4, 658-85 (2006). [CrossRef] [Google Scholar]
  17. WHO, World Health Organization Web site. [Online]. (2014) [Google Scholar]
  18. TB/Leprosy Sector, Ed., Ministry of Health; The National TB strategic plan (2010-2015). Putrajaya, Putrajaya: NTLP, (2015). [Google Scholar]
  19. M. G. Garner and S. A. Hamilton, Principles of Epidemiological Modelling, Review Scientific et Technique, 30, 2, 407-416, (2011). [CrossRef] [Google Scholar]
  20. C. S. Childs, S. Florida, P. Health, and M. College, Evaluating the Spatial Relationship Between Remotely-Sensed Urban Vegetation and Tuberculosis in Guayaquil, Ecuador, 5, (2013). [Google Scholar]
  21. M. Roberts, Nine challenges for deterministic epidemic models, 10, 49–53, (2016). [Google Scholar]
  22. K. S. Chang,. New York, USA: McGraw Hill, 389-399. (2011) [Google Scholar]
  23. J. Malczewski, On the use of weighted linear combination method in GIS:Common and best practice approcahes, Transanctions in GIS, 4, 50–22, (2000). [Google Scholar]
  24. D. U. Pfeiffer, T. P. Robinson, M. Stevens and D. J. Rogers, Spatial Analysis in Epidemiology, Oxford University Press, USA, (2008). [CrossRef] [Google Scholar]
  25. A. B. Lawson and P. Leimich,Approaches to the space time modelling of infectious disease behaviour, IMA Journal of Mathematics Applied in Medicine and Biology, 17, 1–13, (1999). [CrossRef] [Google Scholar]
  26. P. A. Longley, M. F. Goodchild, D. J. Maguire, and D. W. Rhind, Geographic information systems and science, 2nd ed., John Wiley & Sons Chichester, Ed., (2005). [Google Scholar]

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