The TB MAC systematic literature review of existing academic papers that describe mathematical and economic modelling of TB, a database to support or encourage anybody with an interest in TB modelling. When using this resource, please acknowledge the TB Modelling and Analysis Consortium (TB MAC) in your publication, and do let us know through tb-mac@lshtm.ac.uk.

The file can be downloaded below. This holds all the references in the RIS format, which can be imported directly in most reference manager software packages.

Methods

PubMed was searched using the following search query: (tuberculosis OR TB) AND ((mathem* AND (model OR models)) OR (mathem* modell*) OR (mathem* modeling) OR (modeling OR modelling) OR “Population Dynamics”[MeSH Terms] OR “Population Dynamics” OR “System Dynamics” OR “Computer Simulation” OR “Computer Simulation”[MeSH Terms]OR “epidemiologic* model” OR “tuberculosis model” OR ”TB model” OR “transmission model” OR “dynamic model” OR ((within-host OR immun*) AND model))

Web of Science was searched using the following query: TI=((Tuberculosis OR TB) AND model*)

We also searched mathematical modelling journals for any papers on tuberculosis and scanned references from existing reviews for relevant papers as well as historic TB MAC references. Also, modellers from the TB MAC steering committee (Richard White, Chris Dye, Anna Vassal, Ted Cohen and David Dowdy in 2013, and Rein Houben, Nick Menzies and James Trauer in 2017) kindly made their personal libraries available.

Inclusion criteria: We included all papers describing mathematical modelling as defined by Garnett et al. (Lancet 2011, 378, pg 515-525) which distinguishes between mathematical and statistical models as follows “statistical models are those used to derive parameter estimates from empirical data, and mathematical models are those used to make predictions on the basis of those parameter estimates.

With regard to economic modelling, we limited to papers where modelling methods were used to simulate a population or individuals as they progressed through an algorithm (e.g. decision trees).

We excluded papers with a non-human host only, such as bovine TB in badgers or cattle. We also excluded PKPD models.

Period covered, updates and feedback

The databases and literature were searched up to the 31st of October 2017, and the paper collection should be complete up to that date. However, if you know any papers, new or old, that should be included, please contact us at tb-mac@lshtm.ac.uk. Your contributions are much appreciated.

Email tb-mac@lsthm.ac.uk to let us know what you think, to give suggestions on how to improve this resource, or to ask a question.

Additional reviews

TB Diagnostics Modelling

This review collates all the TB modelling papers on novel diagnostics, which was used for the second TB MAC meeting held in April 2013 in Amsterdam. The file can be downloaded below. This holds all the references in the RIS format, which can be imported directly in most reference manager software packages.

Scope of review: Models that evaluated novel tools to diagnose active TB. Not models that explore impact of screening new populations (e.g. Active versus Passive Case Finding). Review focuses on the models comparing diagnostic tool or methods of TB diagnostic modelling, not populations to diagnose.

Papers were selected from the general TB MAC resource of all TB modelling database. A free text search for ‘diagn’ was done to identify potentially relevant papers.

Inclusion/Exclusion criteria

  1. Excluded if:
    1. Focus on diagnosing latent TB disease to fit with scope of meeting
    2. Published before 2000 (so to reflect current modelling practices and reasonably novel diagnostic methods)
    3. Used only diagnostic tools that fall within existing standards of care at the time of analysis. Note: Xpert not considered standard of care for this review
  2. Include cost-effectiveness papers only if reported use of decision or markov model in analysis

Paper selection and data extraction done by 2 reviewers (Alice Zwerling, Rein Houben). From 436 records we selected 92 papers for full-text review, of which 31 papers were included for data extraction.

TB HIV modelling

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