Skip to main content

Advertisement

Log in

An Age-Structured Model for the Potential Impact of Generalized Access to Antiretrovirals on the South African HIV Epidemic

  • Original Article
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

A simple mathematical model (Granich et al., Lancet 373:48–57, 2009) suggested recently that annual HIV testing of the population, with all detected HIV+ individuals immediately treated with antiretrovirals, could lead to the long-term decline of HIV in South Africa and could save millions of lives in the next few years. However, the model suggested that the long-term decline of HIV could not be achieved with less frequent HIV testing. Many observers argued that an annual testing rate was very difficult in practice. Small scale trials are nevertheless in preparation. In this paper, we use a more realistic age-structured model, which suggests that the recent high levels of reported condom use could already lead to a long-term decline of HIV in South Africa. The model therefore suggests that trials with for example 20% of the population tested each year would also be interesting. They would have similar (though smaller) advantages in terms of reduction of mortality and incidence, would be much easier to generalize to larger populations, and would not lead to long term persistence of HIV. Our model simulations also suggest that the age distribution of incidence has changed considerably over the past 20 years in South Africa. This raises some concern about an assumption presently used in EPP/Spectrum, the software used by UNAIDS for its estimates.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anderson, B.A., Phillips, H.E., 2006. Adult mortality (age 15–64) based on death notification data in South Africa: 1997–2004. Report No. 03-09-05, Statistics South Africa, Pretoria. www.statssa.gov.za.

  • Anderson, R.M., May, R.M., 1990. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford.

    Google Scholar 

  • Bacaër, N., Ouifki, R., Pretorius, C., et al., 2008. Modeling the joint epidemics of TB and HIV in a South African township. J. Math. Biol. 57, 557–593.

    Article  MathSciNet  MATH  Google Scholar 

  • Bongaarts, J., 2007. Late marriage and the HIV epidemic in sub-Saharan Africa. Popul. Stud. (Camb.) 61(1), 73–83.

    Article  Google Scholar 

  • Department of Health of South Africa, 2001. National HIV and Syphilis Sero-prevalence Survey of women attending Public Antenatal Clinics in South Africa 2000. www.doh.gov.za.

  • Department of Health, Medical Research Council, Measure DHS+, 2002. South Africa Demographic and Health Survey 1998, Department of Health, Pretoria. www.doh.gov.za/facts/1998/sadhs98/.

  • Department of Health of South Africa, 2003. National HIV and Syphilis Antenatal Sero-prevalence Survey in South Africa 2002. www.doh.gov.za.

  • Department of Health of South Africa, 2004. National HIV and Syphilis Antenatal Sero-prevalence Survey in South Africa 2003. www.doh.gov.za.

  • Department of Health of South Africa, 2006. National HIV and Syphilis Antenatal Sero-prevalence Survey in South Africa 2005. www.doh.gov.za.

  • Department of Health of South Africa, 2009. 2008 National Antenatal Sentinel HIV and Syphilis Prevalence Survey, South Africa. www.doh.gov.za.

  • Dorrington, R., Johnson, L., Bradshaw, D., et al., 2006. The demographic impact of HIV/AIDS in South Africa: National and Provincial Indicators for 2006, Centre for Actuarial Research, South African Medical Research Council and The Actuarial Society of South Africa. www.mrc.ac.za/bod/DemographicImpactHIVIndicators.pdf.

  • Dusheiko, G.M., Brink, B.A., Conradie, J.D., et al., 1989. Regional prevalence of hepatitis B, delta, and human immunodeficiency virus infection in southern Africa: a large population survey. Am. J. Epidemiol. 129, 138–145.

    Google Scholar 

  • Ferrand, R.A., Corbett, E.L., Wood, R., et al., 2009. AIDS among older children and adolescents in Southern Africa: projecting the time course and magnitude of the epidemic. AIDS 23, 2039–2046.

    Article  Google Scholar 

  • Granich, R.M., Gilks, C.F., Dye, C., et al., 2009. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 373(9657), 48–57.

    Article  Google Scholar 

  • Hallett, T.B., Zaba, B., Todd, J., et al., 2008. Estimating incidence from prevalence in generalized epidemics: Method and validation. PLoS Med. 5(4), e80.

    Article  Google Scholar 

  • Hargrove, J., 2008. Migration, mines and mores: the HIV epidemic in southern Africa. South African J. Sci. 104, 53–61.

    Google Scholar 

  • Johnson, L.F., Dorrington, R.E., 2006. Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions. Demogr. Res. 14, 541–573.

    Article  Google Scholar 

  • Johnson, L.F., Dorrington, R.E., Bradshaw, D., et al., 2009. Sexual behaviour patterns in South Africa and their association with the spread of HIV: Insights from a mathematical model. Demogr. Res. 21, 289–339.

    Article  Google Scholar 

  • Shisana, O., Simbayi, L., 2002. Nelson Mandela HSRC Study of HIV/AIDS. HSRC Press, Cape Town. www.hsrcpress.ac.za.

    Google Scholar 

  • Shisana, O., Rehle, T., Simbayi, L., et al., 2005. South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey 2005. HSRC Press, Cape Town. www.hsrcpress.ac.za.

    Google Scholar 

  • Shisana, O., Rehle, T., Simbayi, L., et al., 2008. South African National HIV Prevalence, Incidence, Behaviour and Communication Survey 2008. HSRC Press, Cape Town. www.hsrcpress.ac.za.

    Google Scholar 

  • Statistics South Africa, 2009. Mortality and causes of death in South Africa, 2007: Findings from death notification. www.statssa.gov.za.

  • Stover, J., 2009. AIM: A computer program for making HIV/AIDS projections and examining the demographic and social impacts of AIDS, Futures Group International, Washington DC. http://software.futuresgroup.com/Spectrum/AimmanE.pdf.

  • UNAIDS, 2008. Report on the global AIDS epidemic. www.unaids.org.

  • United Nations Staistics Division, 2009. UNData. http://data.un.org.

  • US Census Bureau, 2009. International Database. www.census.gov/ipc/www/idb/country.php.

  • WHO, UNAIDS, UNICEF, 2009. Towards universal access, scaling up priority HIV/AIDS interventions in the health sector, progress report 2009. www.who.int/hiv/pub/2009progressreport/en/index.html.

  • Williams, B.G., Lloyd-Smith, J.O., Gouws, E., et al., 2006. The potential impact of male circumcision on HIV in Sub-Saharan Africa. PLoS Med. 3(7), 1032–1040.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carel Pretorius.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bacaër, N., Pretorius, C. & Auvert, B. An Age-Structured Model for the Potential Impact of Generalized Access to Antiretrovirals on the South African HIV Epidemic. Bull. Math. Biol. 72, 2180–2198 (2010). https://doi.org/10.1007/s11538-010-9535-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11538-010-9535-2

Keywords

Navigation