{"title":"Evaluating the significance of latent tuberculosis infection treatment in high-incidence countries","authors":"Ankur Gupta, Garhima Arora, Nandadulal Bairagi, Samrat Chatterjee","doi":"10.1002/mma.10532","DOIUrl":null,"url":null,"abstract":"<p>Tuberculosis (TB) caused by <i>Mycobacterium tuberculosis</i> (Mtb) is a devastating and rapidly spreading disease. Despite years of scientific research and numerous efforts, it is still a major and resurgent health problem worldwide, with high mortality rates. Added to the concern is that TB persists as a latent infection, an occult face of TB, that becomes a potential reservoir for active tuberculosis. Since latent TB-infected patients may eventually advance to the active form of TB, an accurate diagnosis and effective treatment of latent tuberculosis are essential for TB control. Latent tuberculosis infection (LTBI) treatment is a prominent component of TB control in low-prevalence countries like the United States; however, its implication in high-incidence countries like India is still challenging. Therefore, the present study aimed to evaluate the impact of implementing diagnosis and treatment of LTBI in high-incidence countries using a mathematical model-based approach. Through our model, we predicted the incidence rate based on the current treatment regimen in India for the year 2035, which is one of the milestones of WHO for a substantial reduction in TB incidence. We observed demographic variability in the effects of various parameters on the TB incidence rate. Finally, we formulated the putative treatment strategies to reduce the TB burden in high-incidence scenarios. Further, we estimated the impact of these proposed treatment strategies on the drug-resistant population in high-incidence scenarios. The model predictions suggested molding the current treatment strategies and focused implementation of LTBI diagnosis and treatment in high-incidence scenarios.</p>","PeriodicalId":49865,"journal":{"name":"Mathematical Methods in the Applied Sciences","volume":"48 3","pages":"4035-4049"},"PeriodicalIF":2.1000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Methods in the Applied Sciences","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mma.10532","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is a devastating and rapidly spreading disease. Despite years of scientific research and numerous efforts, it is still a major and resurgent health problem worldwide, with high mortality rates. Added to the concern is that TB persists as a latent infection, an occult face of TB, that becomes a potential reservoir for active tuberculosis. Since latent TB-infected patients may eventually advance to the active form of TB, an accurate diagnosis and effective treatment of latent tuberculosis are essential for TB control. Latent tuberculosis infection (LTBI) treatment is a prominent component of TB control in low-prevalence countries like the United States; however, its implication in high-incidence countries like India is still challenging. Therefore, the present study aimed to evaluate the impact of implementing diagnosis and treatment of LTBI in high-incidence countries using a mathematical model-based approach. Through our model, we predicted the incidence rate based on the current treatment regimen in India for the year 2035, which is one of the milestones of WHO for a substantial reduction in TB incidence. We observed demographic variability in the effects of various parameters on the TB incidence rate. Finally, we formulated the putative treatment strategies to reduce the TB burden in high-incidence scenarios. Further, we estimated the impact of these proposed treatment strategies on the drug-resistant population in high-incidence scenarios. The model predictions suggested molding the current treatment strategies and focused implementation of LTBI diagnosis and treatment in high-incidence scenarios.
期刊介绍:
Mathematical Methods in the Applied Sciences publishes papers dealing with new mathematical methods for the consideration of linear and non-linear, direct and inverse problems for physical relevant processes over time- and space- varying media under certain initial, boundary, transition conditions etc. Papers dealing with biomathematical content, population dynamics and network problems are most welcome.
Mathematical Methods in the Applied Sciences is an interdisciplinary journal: therefore, all manuscripts must be written to be accessible to a broad scientific but mathematically advanced audience. All papers must contain carefully written introduction and conclusion sections, which should include a clear exposition of the underlying scientific problem, a summary of the mathematical results and the tools used in deriving the results. Furthermore, the scientific importance of the manuscript and its conclusions should be made clear. Papers dealing with numerical processes or which contain only the application of well established methods will not be accepted.
Because of the broad scope of the journal, authors should minimize the use of technical jargon from their subfield in order to increase the accessibility of their paper and appeal to a wider readership. If technical terms are necessary, authors should define them clearly so that the main ideas are understandable also to readers not working in the same subfield.