Infectious and Clinical Tuberculosis Trajectories: Bayesian modeling with case finding implications

AbstractBackgroundThe importance of finding people with undiagnosed tuberculosis (TB) hinges on their clinical and infectious trajectories. Assays for systematic screening should be optimized to find those whose TB will contribute most to future transmission or morbidity.MethodsWe constructed a mathematical model which tracks the disease trajectories of individuals with TB, classifying them over time by bacterial burden (smear positive/negative) and symptom status (symptomatic/subclinical). We used Bayesian methods to calibrate this model to historical survival data and notification, mortality, and prevalence survey data from five countries. We combined the resulting individual disease trajectories with evidence on infectiousness, to compare how much different subsets of prevalent TB contribute to future transmission events.ResultsNearly all (89% [95% uncertainty range 83-93%]) smear-negative subclinical TB resolved before diagnosis or treatment, typically after a short disease course (4.3 [3.3-6.7] months). In contrast, people with smear-positive subclinical TB had a longer overall duration of undiagnosed disease (15.5 [11.0-21.3] months), and most eventually developed symptoms. Despite accounting for only 11-20% of prevalent disease, smear-positive subclinical TB accounted for 37-48% of future transmission – a greater contribution than symptomatic TB or smear-negative TB.ConclusionsSubclinical TB with a high bacterial burden accounts for a disproportionate share of future transmission. Priority should be given to developing inexpensive, easy-to-use assays for screening both symptomatic and asymptomatic individuals at scale – akin to rapid antigen tests for other diseases – even if these assays lack the sensitivity to detect paucibacterial disease.