Department of Applied Mathematics

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Browsing Department of Applied Mathematics by Author "Brown, Lauren"

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    Mathematical modelling of tuberculosis in South Africa : investigating the impact of interventions on population-level incidence and mortality
    (Stellenbosch : Stellenbosch University, 2023-03) Brown, Lauren; Van Schalkwyk, Cari; Marx, Florian; Stellenbosch University. Faculty of Science. Dept. of Applied Mathematics.
    ENGLISH SUMMARY: Background. Tuberculosis (TB) remains a major public health threat in South Africa. Substantial additional efforts are therefore needed to prevent, find, and successfully treat the disease. An increasing number of mathematical modelling studies have investigated the population-level impact of TB prevention and care interventions; however, this evidence has not yet been assessed in the South African context. Of particular concern for TB care in South Africa is the high proportion of initial loss to follow-up (ILTFU), defined as loss to follow-up of individuals who were diagnosed with TB but who did not (yet) initiate TB treatment. The aim of this thesis was to review existing literature on TB mathematical modelling research to determine the most effective intervention strategies to reduce TB burden in South Africa, to identify potential gaps in TB modelling research, and further, to conduct a mathematical modelling study to estimate the impact of reducing ILTFU in South Africa. Methods. A systematic review of studies that used transmission-dynamic models of TB in South Africa was conducted. PubMed, Scopus, and Web of Science databases were searched. Target populations, types of interventions, and estimates of impact on outcomes related to the End TB strategy targets were summarized. For country-level studies, average annual percentage declines (AAPDs) in TB incidence and mortality were estimated to compare the impact of interventions. Additionally, an existing TB transmission-dynamic model was adapted to estimate the impact of reducing ILTFU in South Africa. Data from the LINKEDIn study, a large quasi-experimental study that was conducted in three South African provinces, were used to inform model scenarios and intervention parameter estimates. The impact of scaling-up the LINKEDIn intervention to country level was specified as the number of incident cases and deaths averted over a 13-year period (2023-2035). Results. Twenty-nine studies were identified in the systematic review, of which seven modelled TB preventive interventions, 12 considered interventions along the TB care cascade, and 10 modelled combinations of both. One study considered reductions in TB-related catastrophic costs. The highest impact of a single intervention was estimated in studies of TB vaccination, preventive treatment among people living with HIV, and scale up of antiretroviral treatment. For preventive interventions, AAPDs for incidence varied between 0.06% and 7.07%, and for care-cascade interventions between 0.05% and 3.27%. In the modelling study, reducing ILTFU by 50% in the population was projected to avert 49,812 (95% uncertainty interval [UI]: 21,258-84,644) incident TB cases and 21,479 (UI: 9,500-32,661) deaths between 2023 and 2035. Sensitivity analyses showed that population-level impact would depend on rapid implementation and maximum effect of the intervention. Conclusion. This thesis describes a body of mathematical modelling research with focus on TB prevention and care in South Africa. Higher estimates of impact reported in studies of preventive interventions were found, highlighting the need to invest in TB prevention in South Africa. The population-level impact of reducing ILTFU was projected to be modest. Combinations rather than single interventions, such as the LINKEDIn intervention, are likely needed to reach the End TB Strategy targets in South Africa.