Browsing by Subject "Mathematical models"

Now showing 1 - 6 of 6
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    A comparison of South African national HIV incidence estimates: A critical appraisal of different methods
    (Public Library of Science, 2015) Rehle, Thomas; Johnson, Leigh; Hallett, Timothy; Mahy, Mary; Kim, Andrea; Odido, Helen; Onoya, Dorina; Jooste, Sean; Shisana, Olive; Puren, Adrian
    BACKGROUND: The interpretation of HIV prevalence trends is increasingly difficult as antiretroviral treatment programs expand. Reliable HIV incidence estimates are critical to monitoring transmission trends and guiding an effective national response to the epidemic. Methods and FINDINGS: We used a range of methods to estimate HIV incidence in South Africa: (i) an incidence testing algorithm applying the Limiting-Antigen Avidity Assay (LAg-Avidity EIA) in combination with antiretroviral drug and HIV viral load testing; (ii) a modelling technique based on the synthetic cohort principle; and (iii) two dynamic mathematical models, the EPP/Spectrum model package and the Thembisa model. Overall, the different incidence estimation methods were in broad agreement on HIV incidence estimates among persons aged 15-49 years in 2012. The assay-based method produced slightly higher estimates of incidence, 1.72% (95% CI 1.38 - 2.06), compared with the mathematical models, 1.47% (95% CI 1.23 - 1.72) in Thembisa and 1.52% (95% CI 1.43 - 1.62) in EPP/Spectrum, and slightly lower estimates of incidence compared to the synthetic cohort, 1.9% (95% CI 0.8 - 3.1) over the period from 2008 to 2012. Among youth aged 15-24 years, a declining trend in HIV incidence was estimated by all three mathematical estimation methods. CONCLUSIONS: The multi-method comparison showed similar levels and trends in HIV incidence and validated the estimates provided by the assay-based incidence testing algorithm. Our results confirm that South Africa is the country with the largest number of new HIV infections in the world, with about 1 000 new infections occurring each day among adults aged 15-49 years in 2012.
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    Mathematical models and the fight against diseases in Africa
    (2003) Getz, Wayne M; Gouws, Eleanor; Hahne, Fritz; Kopp, P Ekkehard; Mostert, Paul; Muller, Chris; Seioghe, Cathal; Williams, Brian; Witten, Gareth
    n this age of molecular biology, The healthcare industry, politicians and the community at large are trying to find ‘magic bullet’ drugs and vaccines to conquer disease. Although smallpox has been eradicated and polio may soon be a scourge of the past, many pathogens replicate rapidly and mutate prodigiously, enabling them to evolve ways to circumvent our immune systems, as well as our drugs and vaccines. To fight and win the war against new emerging infections such as HIV/AIDS, TB and now SARS (severe acute respiratory syndrome), it is important to understand the temporal and spatial dynamics of the pathogens in human and, in some cases, animal reservoirs or vector populations. It is also necessary to understand the complex web of socio-economic factors pertinent to controlling the spread of disease, so that feasible, affordable and, most importantly, effective public-health policies can be devised and implemented.
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    Model selection for the dynamics of southern African hake resources
    (1988) Punt, A E; Butterworth, Doug S
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    Modelling the relationship between antiretroviral treatment and HIV prevention: The limits of Spectrum's AIDS Impact Model in a changing policy environment
    (National Inquiry Services Centre, 2007) Nattrass, Nicoli
    This paper shows how two publicly available epidemiological modelling packages, namely the Spectrum AIDS Impact Model and the ASSA2003 AIDS and Demographic Model, predict very different impacts from rolling out highly active antiretroviral treatment (HAART) on new HIV infections. Using South Africa as a case study, it shows that the ASSA2003 model predicts a significant drop in new HIV infections as HAART is rolled out, whereas the Spectrum model assumes that HAART does not have a preventative impact (and in fact generates a small increase in new HIV infections). Users will thus draw different conclusions about the public health benefits of HAART depending on which modelling package they use. Despite being presented as a policy-oriented modelling tool capable of exploring 'what if' questions about the impact of different policy choices, the Spectrum model is illequipped to do so with regard to a HAART rollout. Unlike Spectrum, ASSA2003 is more flexible and its assumptions are clear. Better modelling and more information (including about the relationship between HAART and sexual risk behaviour) is required to develop appropriate public-policy modelling for the HAART era.
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    Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study
    (Public Library of Science, 2013) Estill, Janne; Egger, Matthias; Johnson, Leigh F; Gsponer, Thomas; Wandeler, Gilles; Davies, Mary-Ann; Boulle, Andrew; Wood, Robin; Garone, Daniela; Stringer, Jeffrey S A
    Objectives Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. Design: Mathematical modelling study based on data from ART programmes. METHODS: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. RESULTS: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. CONCLUSIONS: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.
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    Open Access