Profiling Mycobacterium tuberculosis transmission and the resulting disease burden in the five highest tuberculosis burden countries

Abstract

Background: Tuberculosis (TB) control efforts are hampered by an imperfect understanding of TB epidemiology. The true age distribution of disease is unknown because a large proportion of individuals with active TB remain undetected. Understanding of transmission is limited by the asymptomatic nature of latent infection and the pathogen's capacity for late reactivation. A better understanding of TB epidemiology is critically needed to ensure effective use of existing and future control tools.

Methods: We use an agent-based model to simulate TB epidemiology in the five highest TB burden countries-India, Indonesia, China, the Philippines and Pakistan-providing unique insights into patterns of transmission and disease. Our model replicates demographically realistic populations, explicitly capturing social contacts between individuals based on local estimates of age-specific contact in household, school and workplace settings. Time-varying programmatic parameters are incorporated to account for the local history of TB control.

Results: We estimate that the 15-19-year-old age group is involved in more than 20% of transmission events in India, Indonesia, the Philippines and Pakistan, despite representing only 5% of the local TB incidence. According to our model, childhood TB represents around one fifth of the incident TB cases in these four countries. In China, three quarters of incident TB were estimated to occur in the ≥ 45-year-old population. The calibrated per-contact transmission risk was found to be similar in each of the five countries despite their very different TB burdens.

Conclusions: Adolescents and young adults are a major driver of TB in high-incidence settings. Relying only on the observed distribution of disease to understand the age profile of transmission is potentially misleading.

Keywords: Infectious disease; Social mixing; Transmission profile; Tuberculosis.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1

Schematic illustration of the agent-based model. The upper panel represents the structure of the simulated population and the diverse types of contacts simulated (household, school, workplace, other location). The lower panel illustrates individuals’ progression through the various stages of life and infection/disease using diamonds to represent events and boxes for extended phases. Solid arrows indicate deterministic progressions that occur in all surviving individuals, while dashed arrows represent possible but not universal progressions. *Only a fraction of the individuals enters the organised workforce

Fig. 2

Validation of model outputs against prevalence survey estimates for the age-specific TB prevalence in Indonesia (2014), China (2010), the Philippines (2016) and Pakistan (2011). No data were available for the less than 15-year-old individuals from these surveys. Error bars represent the 95% confidence intervals of the survey estimates (in purple) and the 95% simulation intervals resulting from the stochastic variability of the model and the parameter uncertainty (in green)

Fig. 3

Contributions of the various locations to the burden of contact and transmission. Error bars represent the 95% simulation intervals

Fig. 4

Age-specific pattern of social mixing and transmission

Fig. 5

Age distribution of latent tuberculosis infection. Coloured discs should be interpreted as spheres (to increase the relative size of the smaller spheres), with the volume of the spheres being proportional to the following quantities: 2018 total population (grey), size of the LTBI pool in 2018 (green), and number of individuals currently infected in 2018 who will ever develop active TB (purple). The numbers surrounding each disc indicate the age categories represented. Note that LTBI prevalence is predicted to reach extremely high levels among the oldest age category, which is explained by the high historical intensity of transmission in these countries and by the fact that we do not incorporate LTBI clearance

Fig. 6

Age distribution of TB cases. The population age distribution (green) was captured at the starting time of analysis (year 2018). Age of TB cases at activation (red) was recorded over a period of 5 years starting from 2018. Error bars represent the 95% simulation intervals obtained for the TB age distribution