Epidemiological models of Mycobacterium tuberculosis complex infections

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 tuberculosis transmission dynamics, treatment, drug resistance, control strategies for increasing compliance to treatment, HIV/TB co-infection, and patient groups. The models are based on various mathematical systems, such as systems of ordinary differential equations, simulation models, and Markov Chain Monte Carlo methods. The inferences from the models are justified by case studies and statistical analysis of TB patient datasets.

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

“…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.…”

“…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.…”

Tuberculosis in Cape Town: An age-structured transmission model

“…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.…”

Modeling the Spread of Tuberculosis in Semiclosed Communities

“…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.…”