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

M. J. Pitcher, S. A. Dobson, T. W. Kelsey, J. Chaplain, D. J. Sloan, S. H. Gillespie, R. Bowness

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 withinhost dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases a- much can be learned in infection research from these advances.

Original languageEnglish
Pages (from-to)1145-1150
Number of pages6
JournalThe international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
Volume24
Issue number11
DOIs
Publication statusPublished - 1 Nov 2020

Keywords

  • TB modelling
  • disease models
  • statistical modelling
  • within-host mechanistic model

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