Multi-objective parameter fitting.

Published 16th August 2018
Written by Jen Badham (Research Fellow, Centre for Public Health, Queen's University, Belfast)


We developed a prototype agent-based model to compare communication options that encourage people to adopt protective behaviour (such as increased hand washing) during an influenza epidemic. As part of that model, we had complex interactions between epidemic risk, personal characteristics, behaviour of others, and a (simulated) person’s own behaviour. Because of this complexity, statistical fitting of model parameters to the available empirical data was not feasible. Furthermore, the large number of parameters to be adjusted made brute force exploration impractical.


Sandtable helped us to efficiently sample the parameter space and identify sets of parameter values that gave the best model fit. Furthermore, we were able to impose several different conditions for fitting and assess how adjusting parameter values to improve the fit under one condition impacted on other aspects of model fitness.


We were able to determine objectively best parameter values and focus on the key trade-offs between constraints. The rigorous calibration process helped us to identify areas where further modelling work is required. Details of the project and the calibration process are available here.


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