Improving the contribution of mathematical modelling evidence to guidelines and policy: Experiences from tuberculosis
C. Finn McQuaid,
Nicolas A. Menzies,
Rein M.G.J. Houben,
Gabriella B. Gomez,
Anna Vassall,
Nimalan Arinaminpathy,
Peter J. Dodd,
Richard G. White
Affiliations
C. Finn McQuaid
TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; Correspondence to: London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
Nicolas A. Menzies
Department of Global Health and Population, Boston, MA, USA; Center for Health Decision Science, Harvard T H Chan School of Public Health, Boston, MA, USA
Rein M.G.J. Houben
TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
Gabriella B. Gomez
International AIDS Vaccine Initiative, Amsterdam, Netherlands
Anna Vassall
Global Health Economics Centre, London School of Hygiene and Tropical Medicine, London, UK
Nimalan Arinaminpathy
MRC Centre for Global Infectious Disease Analysis, Imperial College, London, UK
Peter J. Dodd
School of Health and Related Research, University of Sheffield, Sheffield, UK
Richard G. White
TB Modelling Group, TB Centre and Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
We read with great interest the recent paper by Lo et al., who argue that there is an urgent need to ensure the quality of modelling evidence used to support international and national guideline development. Here we outline efforts by the Tuberculosis Modelling and Analysis Consortium, together with the World Health Organization Global Task Force on Tuberculosis Impact Measurement, to develop material to improve the quality and transparency of country-level tuberculosis modelling to inform decision-making.
