Skip to main content
SpringerLink
Log in

Dynamics of Tuberculosis in a Developing Country: Nigeria as a Case Study

  • Chapter
  • First Online:
Dynamic Models of Infectious Diseases

  • 1122 Accesses

Abstract

Tuberculosis remains as one of the most dangerous infectious diseases that causes heavy burden on the economy of developing countries and is responsible for more than three million deaths worldwide annually. In order to control tuberculosis, reduce the number of cases and prevent the emergence of drug-resistant strains, the WHO proposed to implement the direct observation therapy strategy (DOTS) in heavy-burden countries. However, the effectiveness of the strategy depends on a number of factors, such as the level of detection and the level of implementation. In this chapter we explore how these factors affect the effectiveness of the strategy. To address this issue, we use simple mathematical models. Analysis of these models allows us to find estimations of the critical levels and to make practically relevant recommendations for the health authorities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ale A, Oyedele A, Falola F, Adepegba A (2007) Infants at risk amid scarcity of BCG vaccine. The Punch Newspaper 47(1376):2

    Google Scholar 

  2. Aparicio JP, Hernandez JC (2006) Preventive treatment of tuberculosis through contact tracing. In: Gumel Abba B (editor-in-chief), Castillo-Chavez Carlos (ed), Mickens Ronald E (ed), Dominic P. Clemence (ed) Mathematical studies on human disease dynamics: emerging paradigms and challenges. AMS contemporary mathematics series, vol 410. American Mathematical Society pp 17–29

    Google Scholar 

  3. Behr MA, Warren SA, Salamon H, Hopewell PC, Ponce de Leon A, Small PM (1999) Transmission of Mycobacterium tuberculosis from patients smear-negative for acid-fast bacilli. Lancet 353:444–449

    Article  PubMed  CAS  Google Scholar 

  4. Bishai WR, Graham NM, Harrington S, Pope DS, Hooper N, Astemborski J, Sheely L, Vlahov D, Glass GE, Chaisson RE (1998) Molecular and geographic patterns of tuberculosis transmission after 15 years of directly observed therapy. J Am Med Assoc 280:1679–1684

    Article  CAS  Google Scholar 

  5. Blower SM, Small PM, Hopewell PC (1996) Control strategies for tuberculosis epidemics: new models for old problems. Science 273(5274):497–500

    Article  PubMed  CAS  Google Scholar 

  6. Borgdorff MW (2004) New measurable indicator for tuberculosis case detection. Emerg Infect Dis 10(9):1523–1528

    Google Scholar 

  7. Borgdorff MW, Floyd K, Broekmans JF (2002) Interventions to reduce tuberculosis mortality and transmission in low and middle-income countries. Bull World Health Organ 80:217–227

    PubMed  Google Scholar 

  8. Castillo-Chavez C, Feng Z (1997) To treat or not to treat: the case of tuberculosis. J Math Biol 35:629–656

    Article  PubMed  CAS  Google Scholar 

  9. Castillo-Chavez C, Feng Z (1998) Mathematical models for the disease dynamics of tuberculosis. In: Arino O, Kimmel M (eds) Proceedings of the fourth international conference on mathematical population dynamics, Worlds Scientific, Singapore, 1998

    Google Scholar 

  10. Chan ED, Iseman MD (2002) Current medical treatment for tuberculosis. Brit Med J 325:1282–1286

    Article  PubMed  Google Scholar 

  11. Chaulk CP, Friedman M, Dunning R (2000) Modeling the epidemiology and economics of directly observed therapy in Baltimore. Int J Tuberc Lung Dis 4:201–207

    PubMed  CAS  Google Scholar 

  12. Chaulk CP, Kazandjian VA (1998) Directly observed therapy for treatment completion of pulmonary tuberculosis: consensus statement of the public health tuberculosis guidelines panel. J Am Med Assoc 279:943–948

    Article  CAS  Google Scholar 

  13. Cohn DL, Catlin BJ, Peterson KL, Judson FN, Sbarbaro JA (1990) A 62-dose, 6-month therapy for pulmonary and extrapulmonary tuberculosis: a twice-weekly, directly observed, and cost-effective regimen. Ann Intern Med 112:407–415

    Article  PubMed  CAS  Google Scholar 

  14. Diekmann O, Heesterbeek JA, Metz JAJ (1990) On the definition and the computation of the basic reproductive ratio, R0 in models of infectious diseases in heterogeneous populations. J Math Biol 28:365–382

    Article  PubMed  CAS  Google Scholar 

  15. Ezenwa AO (1985) Community health and safety in the tropics. Safety Sciences, Lagos, Nigeria

    Google Scholar 

  16. Feng Z, Castillo-Chavez C, Capurro AF (2000) A model for tuberculosis with exogenous reinfection. Theor Pop Biol 57:235

    Article  CAS  Google Scholar 

  17. Feng, Z., Huang W, Castillo-Chavez C (2001) On the role of variable latent periods in mathematical models for tuberculosis. J Dyn Differ Equ 13:425–452

    Article  Google Scholar 

  18. Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653

    Article  Google Scholar 

  19. La Salle J, Lefschetz S (1961) Stability by Liapunov’s direct method. Academic, New York

    Google Scholar 

  20. Magombedze G, Garira W, Mwenje E (2006) Modelling the human immune response mechanisms to Mycobacterium tuberculosis infection in the lungs. Math Biosci Eng 3(4):661–682

    Article  PubMed  Google Scholar 

  21. Miller B (1993) Preventive therapy for tuberculosis. Med Clin N Am 77:1263–1275

    PubMed  CAS  Google Scholar 

  22. Muanya C Tuberculosis as a global emergency. The Guardian Newspaper, April 24, 2004, p 17

    Google Scholar 

  23. North RJ, Yu-Jin J (2004) Immunity to tuberculosis. Annu Rev Immunol 22:599–623

    Article  PubMed  CAS  Google Scholar 

  24. Okuonghae D (2008) Modelling the dynamics of tuberculosis in Nigeria. J Nig Assoc Math Phys 12(1):417–430

    Google Scholar 

  25. Okuonghae D, Korobeinikov A (2007) Dynamics of tuberculosis: the effect of Direct Observation Therapy Strategy (DOTS) in Nigeria. Math Modelling Nat Phenomena: Epidemiol 2(1):113–128

    Article  Google Scholar 

  26. Raffalii J, Sepkowitz KA, Armstrong D (1996) Community-based outbreaks of tuberculosis. Arch Int Med 156:1053

    Article  Google Scholar 

  27. Schluger NW, Rom WN (1998) The host immune response to tuberculosis. Am J Respir Crit Care Med 157:679–691

    Article  PubMed  CAS  Google Scholar 

  28. Smith PG, Moss AR (1994) Epidemiology of tuberculosis. In: Bloom BR (ed) Tuberculosis: pathogenesis, protection, and control. ASM Press, Washington, pp 47–59

    Google Scholar 

  29. Song B, Castillo-Chavez C, Aparicio JP (2000) Tuberculosis models with fast and slow dynamics: the role of close and casual contacts. Math Biosci 180:187–205

    Article  Google Scholar 

  30. Soyinka A Nigeria ranks fourth among TB high-burden countries. The Punch Newspaper, January 15, 2007, p 3

    Google Scholar 

  31. Ssematimba A, Mugisha JYT, Luboobi LS (2005) Mathematical models for the dynamics of tuberculosis in density-dependent populations: the case of Internally Displaced Peoples Camps (IDPCs) in Uganda. J Math Stat 1(3):217–224

    Article  Google Scholar 

  32. Styblo K (1991) Selected papers: epidemiology of tuberculosis. Roy Neth Tuberc Assoc 24:55–62

    Google Scholar 

  33. Styblo K, Bumgarner JR (1991) Tuberculosis can be controlled with existing technologies: evidence. Tuberculosis Surveillance Research Unit, The Hague, pp 60–72

    Google Scholar 

  34. van den Driessche P, Watmough J (2002) Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci 180:29–48

    Article  PubMed  Google Scholar 

  35. WHO (2006) Global tuberculosis control. WHO Report, Geneva

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Benin, PMB 1154, Benin City, Edo State, Nigeria

    Daniel Okuonghae

  2. Centre de Recerca Matematica, Campus de Bellaterra, Edifici C, 08193, Bellaterra, Barcelona, Spain

    Andrei Korobeinikov

Authors
  1. Daniel Okuonghae
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrei Korobeinikov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Okuonghae .

Editor information

Editors and Affiliations

  1. Foundation for Scientific Research and Technological Innovation, Hyderabad, Andhra Pradesh, India

    V. Sree Hari Rao

  2. University of New Mexico School of Medicine, Albuquerque, New Mexico, USA

    Ravi Durvasula

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Okuonghae, D., Korobeinikov, A. (2013). Dynamics of Tuberculosis in a Developing Country: Nigeria as a Case Study. In: Sree Hari Rao, V., Durvasula, R. (eds) Dynamic Models of Infectious Diseases. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9224-5_3

Download citation

Publish with us

Policies and ethics

Search

Navigation