Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 34893600.
- Also identified by DOI 10.1038/s41467-021-27486-z and PMC identifier 8664949.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Computer Simulation
- Malaria
- Models, Biological