Abstract:The resurgence of tuberculosis in the 1990s and the emergence of drug-resistant tuberculosis in the first decade of the 21st century increased the importance of epidemiological models for the disease. Due to slow progression of tuberculosis, the transmission dynamics and its long-term effects can often be better observed and predicted using simulations of epidemiological models. This study provides a review of earlier study on modeling different aspects of tuberculosis dynamics. The models simulate tuberculosi… Show more
“…These models represent the mechanisms of transmission, natural history, and health system interactions that generate tuberculosis outcomes. 13,14 Despite more than a century of epidemiological research into tuberculosis, concrete evidence for these underlying processes is imperfect, 15 and studies have taken various approaches for constructing and parameterising transmission models. This variation can be consequential: in a modelling collaboration examining the post-2015 End TB Strategy, 16 variation in epidemiological assumptions was identified as a cause of the wide range of estimates produced for the health impact 17 and cost-effectiveness 18 of expanded tuberculosis control.…”
Section: Introductionmentioning
confidence: 99%
“…This variation can be consequential: in a modelling collaboration examining the post-2015 End TB Strategy, 16 variation in epidemiological assumptions was identified as a cause of the wide range of estimates produced for the health impact 17 and cost-effectiveness 18 of expanded tuberculosis control. Several reviews 13,14,19 have described standard tuberculosis modelling approaches, and methodological studies 20–25 have examined specific modelling approaches. However, little systematic investigation has been done of assumptions made by published tuberculosis models.…”
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.
“…These models represent the mechanisms of transmission, natural history, and health system interactions that generate tuberculosis outcomes. 13,14 Despite more than a century of epidemiological research into tuberculosis, concrete evidence for these underlying processes is imperfect, 15 and studies have taken various approaches for constructing and parameterising transmission models. This variation can be consequential: in a modelling collaboration examining the post-2015 End TB Strategy, 16 variation in epidemiological assumptions was identified as a cause of the wide range of estimates produced for the health impact 17 and cost-effectiveness 18 of expanded tuberculosis control.…”
Section: Introductionmentioning
confidence: 99%
“…This variation can be consequential: in a modelling collaboration examining the post-2015 End TB Strategy, 16 variation in epidemiological assumptions was identified as a cause of the wide range of estimates produced for the health impact 17 and cost-effectiveness 18 of expanded tuberculosis control. Several reviews 13,14,19 have described standard tuberculosis modelling approaches, and methodological studies 20–25 have examined specific modelling approaches. However, little systematic investigation has been done of assumptions made by published tuberculosis models.…”
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.
“…Despite being of such importance in TB epidemiology, most South African TB models (Aparicio and Castillo-Chavez, 2009; Bacaer et al, 2008; Bhunu et al, 2009; Blower et al, 1995; Castillo-Chavez and Feng, 1997; Hickson et al, 2012; Mills et al, 2011; Ozcaglar et al, 2012; Rodrigues et al, 2007; Roeger et al, 2009; Williams et al, 2010) do not include age, or include age only for HIV incidence but not for rates of progression from latent Mtb infection to active disease or Mtb transmission. We chose to model Cape Town because of the high quality of TB notification data, and the high rates of HIV testing.…”
Background
Tuberculosis (TB) is the leading cause of death in South Africa. The burden of disease varies by age, with peaks in TB notification rates in the HIV-negative population at ages 0-5, 20-24 and 45-49 years. There is little variation between age groups in the rates in the HIV-positive population. The drivers of this age pattern remain unknown.
Methods
We developed an age-structured simulation model of Mycobacterium tuberculosis (Mtb) transmission in Cape Town, South Africa. We considered five states of TB progression: susceptible, infected (latent TB), active TB, treated TB and treatment default. Latently infected individuals could be re-infected; a previous Mtb infection slowed progression to active disease. We further considered three states of HIV progression: HIV negative, HIV positive, on antiretroviral therapy. To parameterize the model, we analysed treatment outcomes from the Cape Town electronic TB register, social mixing patterns from a Cape Town community and literature estimates for other parameters. To investigate the main drivers behind the age patterns, we conducted sensitivity analyses on all parameters related to the age structure.
Results
The model replicated the age patterns in HIV-negative TB notification rates of Cape Town in 2009. Simulated TB notification rate in HIV-negative patients was 1,000/100,000 person-years (pyrs) in children aged < 5 years and decreased to 51/100,000 in children 5-15 years. The peak in early adulthood occurred at 25-29 years (463/100,000 pyrs). After a subsequent decline, simulated TB notification rates gradually increased from the age of 30 years. Sensitivity analyses showed that the dip after the early adult peak was due to the protective effect of latent TB and that retreatment TB was mainly responsible for the rise in TB notification rates from the age of 30 years.
Conclusion
The protective effect of a first latent infection on subsequent infections and the faster progression in previously treated patients are the key determinants of the age-structure of TB notification rates in Cape Town.
“…For review, see, for example, [1, 2] and references therein. The choice of a particular model is strongly connected to the questions we want to answer, and in the present work we will address the problem of long-term dynamics of tuberculosis and latent tuberculosis (LTB) in semiclosed communities.…”
We address the problem of long-term dynamics of tuberculosis (TB) and latent tuberculosis (LTB) in semiclosed communities. These communities are congregate settings with the potential for sustained daily contact for weeks, months, and even years between their members. Basic examples of these communities are prisons, but certain urban/rural communities, some schools, among others could possibly fit well into this definition. These communities present a sort of ideal conditions for TB spread. In order to describe key relevant dynamics of the disease in these communities, we consider a five compartments SEIR model with five possible routes toward TB infection: primary infection after a contact with infected and infectious individuals (fast TB), endogenous reactivation after a period of latency (slow TB), relapse by natural causes after a cure, exogenous reinfection of latently infected, and exogenous reinfection of recovered individuals. We discuss the possible existence of multiple endemic equilibrium states and the role that the two types of exogenous reinfections in the long-term dynamics of the disease could play.
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