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Fig. 1 | BMC Medicine

Fig. 1

From: Achieving a “step change” in the tuberculosis epidemic through comprehensive community-wide intervention: a model-based analysis

Fig. 1

Schematic representation of the modeling approach. We use a compartmental modeling framework to incorporate A natural history of tuberculosis (TB), B age structure, and C risk groups. A Natural history was captured by modeling transition of individuals between six states: uninfected; two stages of latent TB infection (LTBI), early LTBI and late LTBI; two states of active TB disease, asymptomatic and symptomatic; and a recovered state. Uninfected individuals develop early LTBI upon acquiring TB infection, which can either stabilize to become late LTBI or progress early to active TB disease. Individuals with late LTBI can also develop active TB, through a late progression that occurs at a slower rate. Active TB is assumed to start in an asymptomatic form, which can either progress to a symptomatic form or resolve spontaneously to the recovered state without treatment. Symptomatic TB can either be diagnosed and treated or regress back to the asymptomatic form. Populations with late LTBI or who have recovered from previous TB disease can be reinfected (i.e., return to the early LTBI state) but are assumed to have partial immunity. Births and deaths, including TB-related deaths, are included in the model, but not shown here. Active case finding (followed by successful treatment) is modeled as a transition from the two active TB disease states to the recovered state; preventive therapy (and successful resolution of LTBI) is modeled as a transition from the LTBI states to recovered. B The population was subdivided into two groups based on age: children below 15 years and adults 15 years and above. Populations in the two age groups were modeled to have different TB prevalence (reflecting differences in natural history) and to be targeted differentially with the intervention. C The population was modeled to be living in either a high-risk area or other lower-risk areas of the city, with intermixing between the subpopulations, and with different TB transmission and diagnosis rates resulting in different TB prevalence in the two subpopulations

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