Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Theresa Reiker, Monica Golumbeanu, Andrew Shattock, Lydia Burgert, Thomas A. Smith, Sarah Filippi, Ewan Cameron and Melissa A. Penny ()
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Theresa Reiker: Swiss Tropical and Public Health Institute
Monica Golumbeanu: Swiss Tropical and Public Health Institute
Andrew Shattock: Swiss Tropical and Public Health Institute
Lydia Burgert: Swiss Tropical and Public Health Institute
Thomas A. Smith: Swiss Tropical and Public Health Institute
Sarah Filippi: Imperial College London
Ewan Cameron: University of Oxford
Melissa A. Penny: Swiss Tropical and Public Health Institute

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

Date: 2021
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DOI: 10.1038/s41467-021-27486-z

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