@article{f050180602b141a28daf70006c34d437,
title = "Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria",
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.",
author = "Theresa Reiker and Monica Golumbeanu and Andrew Shattock and Lydia Burgert and Smith, {Thomas A.} and Sarah Filippi and Ewan Cameron and Penny, {Melissa A.}",
note = "Funding Information: We acknowledge and thank our colleagues in the Swiss TPH Disease Modeling unit. Calculations were performed at sciCORE (http://scicore.unibas.ch/) scientific computing core facility at University of Basel. The work was funded by the Swiss National Science Foundation through SNSF Professorship of M.A.P. (PP00P3_170702) supporting M.A.P., M.G., and L.B. T.R. was supported by Bill & Melinda Gates Foundation Project OPP1032350 to T.A.S. EC{\textquoteright}s research is supported by funding from the Bill and Melinda Gates Foundation to Curtin University (Opportunity ID: OPP1197730). Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
month = dec,
doi = "10.1038/s41467-021-27486-z",
language = "English",
volume = "12",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group - Macmillan Publishers",
number = "1",
}
