Abstract
Tuberculosis remains one of the most significant bacterial diseases globally and is exacerbated by a growing problem with antibiotic drug resistance. To control the spread of such resistance, it is important for public health authorities to know whether drug-resistant strains in their population have arisen from the ineffective treatment of a previously drug-sensitive case or from the transmission of already existing resistant cases. Data from outbreaks of tuberculosis frequently includes for each isolate, both genotypic information from a molecular marker and information about its drug resistance status. These data can be highly structured due to information about the evolution of the molecular marker, as well as the acquisition of drug resistance. In this chapter, we use this information to estimate key parameters related to drug resistance, such as the proportion of drug-resistant cases arising as a result of treatment failure or transmission.
| Original language | English |
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| Title of host publication | Data Driven Science for Clinically Actionable Knowledge in Diseases |
| Editors | Daniel R. Catchpoole, Simeon J. Simoff, Paul J. Kennedy, Quang Vinh Nguyen |
| Place of Publication | U.S. |
| Publisher | CRC Press |
| Chapter | 3 |
| Pages | 64-91 |
| Number of pages | 28 |
| ISBN (Electronic) | 9781003292357 |
| ISBN (Print) | 9781032273532 |
| DOIs | |
| Publication status | Published - 2024 |