| Title |
A user-friendly mathematical modelling web interface to assist local decision making in the fight against drug-resistant tuberculosis
|
|---|---|
| Published in |
BMC Infectious Diseases, May 2017
|
| DOI | 10.1186/s12879-017-2478-6 |
| Pubmed ID | |
| Authors |
Romain Ragonnet, James M. Trauer, Justin T. Denholm, Ben J. Marais, Emma S. McBryde |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 14% |
| Student > Ph. D. Student | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Postgraduate | 3 | 7% |
| Student > Bachelor | 2 | 5% |
| Other | 8 | 18% |
| Unknown | 17 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 16% |
| Mathematics | 3 | 7% |
| Nursing and Health Professions | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Social Sciences | 2 | 5% |
| Other | 5 | 11% |
| Unknown | 22 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2017.
All research outputs
#21,264,673
of 23,881,329 outputs
Outputs from BMC Infectious Diseases
#6,704
of 7,931 outputs
Outputs of similar age
#278,406
of 318,026 outputs
Outputs of similar age from BMC Infectious Diseases
#153
of 183 outputs
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