How can mathematical models advance tuberculosis control in high HIV prevalence settings?

Existing approaches to tuberculosis (TB) control have been no more than partially successful in areas with high human immunodeficiency virus (HIV) prevalence. In the context of increasingly constrained resources, mathematical modelling can augment understanding and support policy for implementing those strategies that are most likely to bring public health and economic benefits. In this paper, we present an overview of past and recent contributions of TB modelling in this key area, and suggest a way forward through a modelling research agenda that supports a more effective response to the TB-HIV epidemic, based on expert discussions at a meeting convened by the TB Modelling and Analysis Consortium. The research agenda identified high-priority areas for future modelling efforts, including 1) the difficult diagnosis and high mortality of TB-HIV; 2) the high risk of disease progression; 3) TB health systems in high HIV prevalence settings; 4) uncertainty in the natural progression of TB-HIV; and 5) combined interventions for TB-HIV. Efficient and rapid progress towards completion of this modelling agenda will require co-ordination between the modelling community and key stakeholders, including advocates, health policy makers, donors and national or regional finance officials. A continuing dialogue will ensure that new results are effectively communicated and new policy-relevant questions are addressed swiftly

Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology.Comment: 28 pages, 11 figures, 3 table

Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions

Mathematical modelling is commonly used to evaluate infectious disease control policy and is influential in shaping policy and budgets. Mathematical models necessarily make assumptions about disease natural history and, if these assumptions are not valid, the results of these studies can be biased. We did a systematic review of published tuberculosis transmission models to assess the validity of assumptions about progression to active disease after initial infection (PROSPERO ID CRD42016030009). We searched PubMed, Web of Science, Embase, Biosis, and Cochrane Library, and included studies from the earliest available date (Jan 1, 1962) to Aug 31, 2017. We identified 312 studies that met inclusion criteria. Predicted tuberculosis incidence varied widely across studies for each risk factor investigated. For population groups with no individual risk factors, annual incidence varied by several orders of magnitude, and 20-year cumulative incidence ranged from close to 0% to 100%. A substantial proportion of modelled results were inconsistent with empirical evidence: for 10-year cumulative incidence, 40% of modelled results were more than double or less than half the empirical estimates. These results demonstrate substantial disagreement between modelling studies on a central feature of tuberculosis natural history. Greater attention to reproducing known features of epidemiology would strengthen future tuberculosis modelling studies, and readers of modelling studies are recommended to assess how well those studies demonstrate their validity

Design and Analysis of Infectious Disease Studies

The fourth workshop on this theme is devoted to the statistical problems of planning and analyzing studies in infectious disease epidemiology

Healthcare seeking behaviour as a link between tuberculosis and socioeconomic factors

Socioeconomic barriers to tuberculosis care-seeking and costs due to care-seeking lead to unfavourable treatment, epidemiological and economic outcomes. Especially in the post-2015 era, socioeconomic interventions for tuberculosis control are receiving increasing attention. In Taiwan, the National Health Insurance programme minimises out-of-pocket expenses for patients, but important delays to tuberculosis treatment still exist. Based on the population and tuberculosis epidemiology in Taiwan, I develop an analysis for profiling the efficacy of tuberculosis care provision and patients' care-seeking pathways. The results highlight that the interrupted tuberculosis evaluation processes and low diagnostic capacity in small local hospitals stands as key causes of extended delays to treatment, unfavourable outcomes, and costs. I analyse socioeconomic status (SES) of employment, vulnerability, and residential contexts, to identify risk factors for different aspects of care-seeking. To link the care-seeking pathways to the nationwide tuberculosis epidemiology, I develop a data-driven hybrid simulation model. The model integrates the advantages of agent-based approaches in representing detail, and equation-based approaches in simplicity and low computational cost. This approach makes feasible Monte-Carlo experiments for robust inferences without over-simplifying the care-seeking details of interest. By comparing the hybrid model simulations with a corresponding equation-based comparator, I confirm its validity. I considered interventions to improve universal health coverage by decentralising tuberculosis diagnostic capacity. I modelled specific interventions increasing the coverage of tuberculosis diagnostic capacity using various SES-targeted scale-up strategies. These show potential benefits in terms of reducing dropouts and reducing the tuberculosis burden, without significant increases in the inequality of care-seeking costs. I suggest considering additional SES variables such as education, health illiteracy, and social segregation to find other care-seeking barriers. Further investigations of SES-related interventions against tuberculosis, including formal impact and health economic evaluation, should be pursued in collaboration with policymakers able to advise on feasibility and patients able to advise on acceptability

TB STIGMA – MEASUREMENT GUIDANCE

TB is the most deadly infectious disease in the world, and stigma continues to play a significant role in worsening the epidemic. Stigma and discrimination not only stop people from seeking care but also make it more difficult for those on treatment to continue, both of which make the disease more difficult to treat in the long-term and mean those infected are more likely to transmit the disease to those around them. TB Stigma – Measurement Guidance is a manual to help generate enough information about stigma issues to design and monitor and evaluate efforts to reduce TB stigma. It can help in planning TB stigma baseline measurements and monitoring trends to capture the outcomes of TB stigma reduction efforts. This manual is designed for health workers, professional or management staff, people who advocate for those with TB, and all who need to understand and respond to TB stigma

Contact tracing - Old models and new challenges

Contact tracing is an effective method to control emerging infectious diseases. Since the 1980's, modellers are developing a consistent theory for contact tracing, with the aim to find effective and efficient implementations, and to assess the effects of contact tracing on the spread of an infectious disease. Despite the progress made in the area, there remain important open questions. In addition, technological developments, especially in the field of molecular biology (genetic sequencing of pathogens) and modern communication (digital contact tracing), have posed new challenges for the modelling community. In the present paper, we discuss modelling approaches for contact tracing and identify some of the current challenges for the field

Who dies where, when and why? Modelling determinants and space-time risk of infant, child and adult mortality in rural South Africa, 1992-2008

Ph.D., University of the Witwatersrand, Faculty of Health Sciences, 201