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Epidemics. 2017 Dec;21:39-47. doi: 10.1016/j.epidem.2017.06.002. Epub 2017 Jun 16.

Optimally capturing latency dynamics in models of tuberculosis transmission.

Author information

1
Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia; Burnet Institute, Australia. Electronic address: romain.ragonnet@burnet.edu.au.
2
Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia; School of Population Health and Preventive Medicine, Monash University, Australia; Victorian Tuberculosis Program, Melbourne, Australia.
3
Burnet Institute, Australia; School of Population Health and Preventive Medicine, Monash University, Australia.
4
Australian Institute of Tropical Health and Medicine, James Cook University, Australia.
5
Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia; Victorian Tuberculosis Program, Melbourne, Australia; Royal Melbourne Hospital, Melbourne, Australia.
6
Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Australia; Australian Institute of Tropical Health and Medicine, James Cook University, Australia.

Abstract

Although different structures are used in modern tuberculosis (TB) models to simulate TB latency, it remains unclear whether they are all capable of reproducing the particular activation dynamics empirically observed. We aimed to determine which of these structures replicate the dynamics of progression accurately. We reviewed 88 TB-modelling articles and classified them according to the latency structure employed. We then fitted these different models to the activation dynamics observed from 1352 infected contacts diagnosed in Victoria (Australia) and Amsterdam (Netherlands) to obtain parameter estimates. Six different model structures were identified, of which only those incorporating two latency compartments were capable of reproducing the activation dynamics empirically observed. We found important differences in parameter estimates by age. We also observed marked differences between our estimates and the parameter values used in many previous models. In particular, when two successive latency phases are considered, the first period should have a duration that is much shorter than that used in previous studies. In conclusion, structures incorporating two latency compartments and age-stratification should be employed to accurately replicate the dynamics of TB latency. We provide a catalogue of parameter values and an approach to parameter estimation from empiric data for calibration of future TB-models.

KEYWORDS:

Mathematical modelling; Parameter estimation; Risk of disease activation; Tuberculosis latency

PMID:
28641948
DOI:
10.1016/j.epidem.2017.06.002
[Indexed for MEDLINE]
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