TIME Impact - a new user-friendly tuberculosis (TB) model to inform TB policy decisions.

Tuberculosis (TB) is the leading cause of death from infectious disease worldwide, predominantly affecting low- and middle-income countries (LMICs), where resources are limited. As such, countries need to be able to choose the most efficient interventions for their respective setting. Mathematical models can be valuable tools to inform rational policy decisions and improve resource allocation, but are often unavailable or inaccessible for LMICs, particularly in TB. We developed TIME Impact, a user-friendly TB model that enables local capacity building and strengthens country-specific policy discussions to inform support funding applications at the (sub-)national level (e.g. Ministry of Finance) or to international donors (e.g. the Global Fund to Fight AIDS, Tuberculosis and Malaria).TIME Impact is an epidemiological transmission model nested in TIME, a set of TB modelling tools available for free download within the widely-used Spectrum software. The TIME Impact model reflects key aspects of the natural history of TB, with additional structure for HIV/ART, drug resistance, treatment history and age. TIME Impact enables national TB programmes (NTPs) and other TB policymakers to better understand their own TB epidemic, plan their response, apply for funding and evaluate the implementation of the response.The explicit aim of TIME Impact's user-friendly interface is to enable training of local and international TB experts towards independent use. During application of TIME Impact, close involvement of the NTPs and other local partners also builds critical understanding of the modelling methods, assumptions and limitations inherent to modelling. This is essential to generate broad country-level ownership of the modelling data inputs and results. In turn, it stimulates discussions and a review of the current evidence and assumptions, strengthening the decision-making process in general.TIME Impact has been effectively applied in a variety of settings. In South Africa, it informed the first South African HIV and TB Investment Cases and successfully leveraged additional resources from the National Treasury at a time of austerity. In Ghana, a long-term TIME model-centred interaction with the NTP provided new insights into the local epidemiology and guided resource allocation decisions to improve impact

HIV/Aids epidemic in India and predicting the impact of the national response: mathematical modeling and analysis

After two phases of AIDS control activities in India, the third phase of the National AIDS Control Programme (NACP III) was launched in July 2007. Our focus here is to predict the number of people living with HIV/AIDS (PLHA) in India so that the results can assist the NACP III planning team to determine appropriate targets to be activated during the project period (2007-2012). We have constructed a dynamical model that captures the mixing patterns between susceptibles and infectives in both low-risk and high-risk groups in the population. Our aim is to project the HIV estimates by taking into account general interventions for susceptibles and additional interventions, such as targeted interventions among high risk groups, provision of anti-retroviral therapy, and behavior change among HIV-positive individuals. Continuing the current level of interventions in NACP II, the model estimates there will be 5.06 million PLHA by the end of 2011. If 50 percent of the targets in NACP III are achieved by the end of the above period then about 0.8 million new infections will be averted in that year. The current status of the epidemic appears to be less severe compared to the trend observed in the late 1990s. The projections based on the second phase and the third phase of the NACP indicate prevention programmes which are directed towards the general and high-risk populations, and HIV-positive individuals will determine the decline or stabilization of the epidemic. Model based results are derived separately for the revised HIV estimates released in 2007. We perform a Monte Carlo procedure for sensitivity analysis of parameters and model validation. We also predict a positive role of implementation of anti-retroviral therapy treatment of 90 percent of the eligible people in the country. We present methods for obtaining disease progression parameters using convolution approaches. We also extend our models to age-structured populations

A mathematical investigation of the effects of sexual orientation and HIV status on HPV transmission and vaccination

The effect of the inclusion of sexual behaviour, particularly three sexual orientation classes, on the transmission dynamics of HPV and cervical cancer incidence was investigated. A comprehensive literature review of mathematical models of HPV transmission and the natural history of cervical cancer was concluded. A mathematical model using ordinary differential equations was developed, which incorporated the three sexual orientation classes, and a sexual mixing algorithm for modelling the transmission dynamics. Reproduction numbers, determined through a simplified version of the developed model, indicated that the bisexual population could form a bridge between the heterosexual and homosexual population. The level of interaction is determined by the selection preferences of a bisexual individual to form a partnership with an individual of the same or opposite sex. The model was simulated, with parameters based on a South African population and HPV type 16/18, to investigate the effects of HIV status, sexual orientation and various vaccination strategies on HPV transmission and cervical cancer incidence. The results indicated that HIV status is a significant factor when determining cervical cancer incidence. The results regarding vaccination strategies agreed with results from the literature review with a two sex before sexual debut and catch up program the most effective, noting that with increased vaccination coverage of females the marginal impact on cervical cancer incidence of this approach diminished

HPTN 071 (PopART): A Cluster-Randomized Trial of the Population Impact of an HIV Combination Prevention Intervention Including Universal Testing and Treatment: Mathematical Model

BACKGROUND: The HPTN 052 trial confirmed that antiretroviral therapy (ART) can nearly eliminate HIV transmission from successfully treated HIV-infected individuals within couples. Here, we present the mathematical modeling used to inform the design and monitoring of a new trial aiming to test whether widespread provision of ART is feasible and can substantially reduce population-level HIV incidence. METHODS AND FINDINGS: The HPTN 071 (PopART) trial is a three-arm cluster-randomized trial of 21 large population clusters in Zambia and South Africa, starting in 2013. A combination prevention package including home-based voluntary testing and counseling, and ART for HIV positive individuals, will be delivered in arms A and B, with ART offered universally in arm A and according to national guidelines in arm B. Arm C will be the control arm. The primary endpoint is the cumulative three-year HIV incidence. We developed a mathematical model of heterosexual HIV transmission, informed by recent data on HIV-1 natural history. We focused on realistically modeling the intervention package. Parameters were calibrated to data previously collected in these communities and national surveillance data. We predict that, if targets are reached, HIV incidence over three years will drop by >60% in arm A and >25% in arm B, relative to arm C. The considerable uncertainty in the predicted reduction in incidence justifies the need for a trial. The main drivers of this uncertainty are possible community-level behavioral changes associated with the intervention, uptake of testing and treatment, as well as ART retention and adherence. CONCLUSIONS: The HPTN 071 (PopART) trial intervention could reduce HIV population-level incidence by >60% over three years. This intervention could serve as a paradigm for national or supra-national implementation. Our analysis highlights the role mathematical modeling can play in trial development and monitoring, and more widely in evaluating the impact of treatment as prevention

Tuberculosis control in South African gold mines: mathematical modeling of a trial of community-wide isoniazid preventive therapy.

A recent major cluster randomized trial of screening, active disease treatment, and mass isoniazid preventive therapy for 9 months during 2006-2011 among South African gold miners showed reduced individual-level tuberculosis incidence but no detectable population-level impact. We fitted a dynamic mathematical model to trial data and explored 1) factors contributing to the lack of population-level impact, 2) the best-achievable impact if all implementation characteristics were increased to the highest level achieved during the trial ("optimized intervention"), and 3) how tuberculosis might be better controlled with additional interventions (improving diagnostics, reducing treatment delay, providing isoniazid preventive therapy continuously to human immunodeficiency virus-positive people, or scaling up antiretroviral treatment coverage) individually and in combination. We found the following: 1) The model suggests that a small proportion of latent infections among human immunodeficiency virus-positive people were cured, which could have been a key factor explaining the lack of detectable population-level impact. 2) The optimized implementation increased impact by only 10%. 3) Implementing additional interventions individually and in combination led to up to 30% and 75% reductions, respectively, in tuberculosis incidence after 10 years. Tuberculosis control requires a combination prevention approach, including health systems strengthening to minimize treatment delay, improving diagnostics, increased antiretroviral treatment coverage, and effective preventive treatment regimens

Dynamic modelling of hepatitis C virus transmission among people who inject drugs: a methodological review

Equipment sharing among people who inject drugs (PWID) is a key risk factor in infection by hepatitis C virus (HCV). Both the effectiveness and cost-effectiveness of interventions aimed at reducing HCV transmission in this population (such as opioid substitution therapy, needle exchange programs or improved treatment) are difficult to evaluate using field surveys. Ethical issues and complicated access to the PWID population make it difficult to gather epidemiological data. In this context, mathematical modelling of HCV transmission is a useful alternative for comparing the cost and effectiveness of various interventions. Several models have been developed in the past few years. They are often based on strong hypotheses concerning the population structure. This review presents compartmental and individual-based models in order to underline their strengths and limits in the context of HCV infection among PWID. The final section discusses the main results of the papers

Mathematical Modeling to Evaluate Disease Control Policy

In this dissertation I assessed three distinct policy questions: the implications of introducing a new tuberculosis diagnostic in southern Africa, the potential value of research related to HIV treatment policy in South Africa, and the causal effect of state cigarette taxes imposed between 1996 and 2013 on health outcomes in the United States