Data for: Calibration of individual-based models to epidemiological data: a systematic review

Calibrating or fitting an individual-based model (IBM) to data is a
crucial step in model development. We performed a systematic review to
provide an overview of calibration methods used in IBMs modelling
infectious disease spread. We included articles if models stored
individual-specific information and calibration involved running the model
and comparing model output to population-level targets expressed as
summary statistics. The dataset contains information for each of the
included articles on model calibration methods, including; the
parameter-search strategy, the goodness-of-fit measure, acceptance
criteria, and stopping rules. Also, the dataset contains information on
contextual variables for model calibration such as target statistics and
parameters.