Optimally capturing latency dynamics in models of tuberculosis transmission

Ragonnet, Romain, Trauer, James M., Scott, Nick, Meehan, Michael T., and McBryde, Emma S. (2017) Optimally capturing latency dynamics in models of tuberculosis transmission. Epidemics, 21. pp. 39-47.

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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.

Item ID: 51098
Item Type: Article (Research - C1)
ISSN: 1878-0067
Keywords: tuberculosis latency, mathematical modelling, risk of disease activation, parameter, estimation
Additional Information:

© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)

Funders: Australian Government Research Training Program
Date Deposited: 10 Oct 2017 23:33
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110309 Infectious Diseases @ 50%
01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010202 Biological Mathematics @ 50%
SEO Codes: 92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920109 Infectious Diseases @ 50%
97 EXPANDING KNOWLEDGE > 970111 Expanding Knowledge in the Medical and Health Sciences @ 50%
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