A user-friendly mathematical modelling web interface to assist local decision making in the fight against drug-resistant tuberculosis

Ragonnet, Romain, Trauer, James M., Denholm, Justin T., Marais, Ben J., and McBryde, Emma S. (2017) A user-friendly mathematical modelling web interface to assist local decision making in the fight against drug-resistant tuberculosis. BMC Infectious Diseases, 17. 374.

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Abstract

Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) represent an important challenge for global tuberculosis (TB) control. The high rates of MDR/RR-TB observed among re-treatment cases can arise from diverse pathways: de novo amplification during initial treatment, inappropriate treatment of undiagnosed MDR/RR-TB, relapse despite appropriate treatment, or reinfection with MDR/RR-TB. Mathematical modelling allows quantification of the contribution made by these pathways in different settings. This information provides valuable insights for TB policy-makers, allowing better contextualised solutions. However, mathematical modelling outputs need to consider local data and be easily accessible to decision makers in order to improve their usefulness. We present a user-friendly web-based modelling interface, which can be used by people without technical knowledge. Users can input their own parameter values and produce estimates for their specific setting. This innovative tool provides easy access to mathematical modelling outputs that are highly relevant to national TB control programs. In future, the same approach could be applied to a variety of modelling applications, enhancing local decision making.

Item ID: 50659
Item Type: Article (Research - C1)
ISSN: 1471-2334
Keywords: user interface, decision making, tuberculosis, multidrug-resistant tuberculosis, re-treatment, causal pathway, misdiagnosis
Additional Information:

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Funders: Australian Government (AG)
Projects and Grants: AG Research Training Program Scholarship
Date Deposited: 20 Sep 2017 10:56
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1108 Medical Microbiology > 110801 Medical Bacteriology @ 100%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response) @ 100%
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