Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer
Danny Scarponi,
Andrew Iskauskas,
Rebecca A. Clark,
Ian Vernon,
Trevelyan J. McKinley,
Michael Goldstein,
Christinah Mukandavire,
Arminder Deol,
Chathika Weerasuriya,
Roel Bakker,
Richard G. White,
Nicky McCreesh
Affiliations
Danny Scarponi
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK; Corresponding author.
Andrew Iskauskas
Department of Mathematical Sciences, Durham University, UK
Rebecca A. Clark
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Ian Vernon
Department of Mathematical Sciences, Durham University, UK
Trevelyan J. McKinley
College of Medicine and Health, University of Exeter, UK
Michael Goldstein
Department of Mathematical Sciences, Durham University, UK
Christinah Mukandavire
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Arminder Deol
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Chathika Weerasuriya
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Roel Bakker
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Richard G. White
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Nicky McCreesh
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
Infectious disease models are widely used by epidemiologists to improve the understanding of transmission dynamics and disease natural history, and to predict the possible effects of interventions. As the complexity of such models increases, however, it becomes increasingly challenging to robustly calibrate them to empirical data. History matching with emulation is a calibration method that has been successfully applied to such models, but has not been widely used in epidemiology partly due to the lack of available software. To address this issue, we developed a new, user-friendly R package hmer to simply and efficiently perform history matching with emulation. In this paper, we demonstrate the first use of hmer for calibrating a complex deterministic model for the country-level implementation of tuberculosis vaccines to 115 low- and middle-income countries. The model was fit to 9–13 target measures, by varying 19–22 input parameters. Overall, 105 countries were successfully calibrated. Among the remaining countries, hmer visualisation tools, combined with derivative emulation methods, provided strong evidence that the models were misspecified and could not be calibrated to the target ranges. This work shows that hmer can be used to simply and rapidly calibrate a complex model to data from over 100 countries, making it a useful addition to the epidemiologist’s calibration tool-kit.