Optimally capturing latency dynamics in models of tuberculosis transmission

Ragonnet, Romain; Trauer, James M.; Scott, Nick; Meehan, Michael T.; Denholm, Justin T.; McBryde, Emma S.

doi:10.1016/j.epidem.2017.06.002

Cited by 32 publications

(30 citation statements)

References 24 publications

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“…A recent study 39 examining different model structures found that structure E performed either worst or second worst of the six structures examined (depending on the fitting method). In our analysis, structure E performed better than structures A, D, and J, but the root mean squared error was still ten times worse than that of the other structures.…”

Section: Discussionmentioning

confidence: 99%

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

Menzies

Wolf

Connors

et al. 2018

The Lancet Infectious Diseases

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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.

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Section: Discussionmentioning

confidence: 99%

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

Menzies

Wolf

Connors

et al. 2018

The Lancet Infectious Diseases

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“…The systematic literature review reported in Menzies et al [6] found that these 3 structures accounted for approximately 70% of all published TB models. Both Menzies et al [6] and Ragonnet et al [5] showed that models 1 and 2 gave equally good fit to the available data on the cumulative incidence of TB following infection and it was not possible to distinguish a "best" model. Both studies also found model 3 gave a significantly worse fit, but as this structure is employed in a large proportion of published modelling studies (approximately 50% based on the literature review in [6]) we included it in our analysis.…”

Section: Selection Of Model Structuresmentioning

confidence: 99%

“…The analysis in Ragonnet et al [5] and Menzies et al [6] compared the various model structures to different data sets and therefore identified different best-fitting parameters. We compare the impact of using these 2 different parameter sources on the model predictions of the impact of preventive therapy.…”

Section: Model Parameterisationmentioning

confidence: 99%

“…Two recent papers [5,6] have compared different model structures to describe the progression from infection to disease and assessed their ability to reproduce observations of the time from M.tb infection to TB disease onset. These analyses suggested that certain model structures capture these dynamics better than others, and that the parameters and structures used in many published modelling studies do not accurately reproduce the temporal pattern of disease following exposure to M.tb observed in many settings.…”

Section: Introductionmentioning

confidence: 99%

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Modelling the impact of tuberculosis preventive therapy: the importance of disease progression assumptions

Sumner

White

2019

Preprint

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BackgroundFollowing infection with Mycobacterium tuberculosis (M.tb) individuals may rapidly develop tuberculosis (TB) disease or enter "latent" infection state with a low risk of progression to disease. The mechanisms underlying this process are incompletely known. Mathematical models use a variety of structures and parameterisations to represent this progression from infection with M.tb to disease. This structural and parametric uncertainty may affect the predicted impact of interventions leading to incorrect conclusions and decision making. MethodsWe used a simple dynamic transmission model to explore the effect of uncertainty in model structure and parameterisation on the predicted impact of scaling up preventive therapy. We compared three commonly used model structures and used parameter values from two different data sources. Models 1 and 2 are equally consistent with observations of the time from infection to disease. Model 3, produces a worse fit to the data, but is widely used in published modelling studies. We simulated treatment of 5% of all M.tb infected individuals per year in a population of 10,000 and calculated the reduction in TB incidence and number needed to treat to avert one TB case over 10 years. ResultsThe predicted impact of the preventive therapy intervention depended on both the model structure and the parameterisation of that structure. For example, at a baseline annual TB incidence of 500/100,000, the impact ranged from 11% to 27% and the number needed to treat to avert one TB case varied between 38 and 124. The relative importance of structure and parameters varied depending on the baseline incidence of TB. DiscussionOur analysis shows that the choice of model structure and the parameterisation can influence the predicted impact of interventions. Modelling studies should consider incorporating structural uncertainty in their analysis. Not doing so may lead to incorrect conclusions on the impact of interventions.

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“…The proportion of infected persons progressing to active disease from early latency was greater for children than adults [11] and each new episode of TB was distinguished according to smear status into smear-positive, smear-negative pulmonary and extrapulmonary. Diabetic persons had an approximately threefold increased risk of progression to active TB following infection (Supplementary Section 14) [13].…”

Section: Methodsmentioning

confidence: 99%

Strategic investment in tuberculosis control in the Republic of Bulgaria

et al. 2019

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As Bulgaria transitions away from Global Fund grant, robust estimates of the comparative impact of the various response strategies under consideration are needed to ensure sustained effectiveness of the tuberculosis (TB) programme. We tailored an established mathematical model for TB control to the epidemic in Bulgaria to project the likely outcomes of seven intervention scenarios. Under existing programmatic conditions projected forward, the country's targets for achieving TB elimination in the coming decades will not be achieved. No interventions under consideration were predicted to accelerate the baseline projected reduction in epidemiological indicators significantly. Discontinuation of the ‘Open Doors’ program and activities of non-governmental organisations would result in a marked exacerbation of the epidemic (increasing incidence in 2035 by 6–8% relative to baseline conditions projected forward). Changing to a short course regimen for multidrug-resistant TB (MDR-TB) would substantially decrease MDR-TB mortality (by 21.6% in 2035 relative to baseline conditions projected forward). Changing to ambulatory care for eligible patients would not affect TB burden but would be markedly cost-saving. In conclusion, Bulgaria faces important challenges in transitioning to a primarily domestically-financed TB programme. The country should consider maintaining currently effective programs and shifting towards ambulatory care to ensure program sustainability.

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Optimally capturing latency dynamics in models of tuberculosis transmission

Cited by 32 publications

References 24 publications

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

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

Modelling the impact of tuberculosis preventive therapy: the importance of disease progression assumptions

Strategic investment in tuberculosis control in the Republic of Bulgaria

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