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BMC Med. 2019 Nov 22;17(1):208. doi: 10.1186/s12916-019-1452-0.

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

Author information

1
Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, Australia. romain.ragonnet@monash.edu.au.
2
School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia. romain.ragonnet@monash.edu.au.
3
Burnet Institute, 85 Commercial Road, Melbourne, VIC, Australia. romain.ragonnet@monash.edu.au.
4
School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia.
5
Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
6
School of Computing and Information Systems, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia.
7
The Peter Doherty Institute for Infection and Immunity, The Royal Melbourne Hospital and The University of Melbourne, Melbourne, VIC, Australia.
8
Burnet Institute, 85 Commercial Road, Melbourne, VIC, Australia.
9
Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC, Australia.
10
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia.

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

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