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Open Access How mechanistic in silico modelling can improve our understanding of TB disease and treatment

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TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases – much can be learned in infection research from these advances.
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Keywords: TB modelling; disease models; statistical modelling; within-host mechanistic model

Document Type: Research Article

Affiliations: 1: School of Computer Science, University of St Andrews, St Andrews, Department of Immunobiology, King´s College London, London 2: School of Computer Science, University of St Andrews, St Andrews 3: School of Mathematics, University of St Andrews, St Andrews 4: School of Medicine, University of St Andrews, St Andrews 5: School of Medicine, University of St Andrews, St Andrews, Department of Mathematical Sciences, University of Bath, Bath, UK

Publication date: November 1, 2020

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  • The International Journal of Tuberculosis and Lung Disease (IJTLD) is for clinical research and epidemiological studies on lung health, including articles on TB, TB-HIV and respiratory diseases such as COVID-19, asthma, COPD, child lung health and the hazards of tobacco and air pollution. Individuals and institutes can subscribe to the IJTLD online or in print – simply email us at [email protected] for details.

    The IJTLD is dedicated to understanding lung disease and to the dissemination of knowledge leading to better lung health. To allow us to share scientific research as rapidly as possible, the IJTLD is fast-tracking the publication of certain articles as preprints prior to their publication. Read fast-track articles.

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