Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda.

ABSTRACT: Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensi

SUBMITTER: Andrianakis I 

PROVIDER: S-EPMC4288726 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature