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. Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration challenging. Here, the authors propose a Bayesian optimization framework to calibrate a complex malaria transmission simulator.
【저자키워드】 Computational platforms and environments, Computational models, Computational science, Parasitic infection, 【초록키워드】 Diseases, Bayesian, Transmission, diagnostics, malaria, Disease progression, sensitivity, dataset, epidemiological, epidemiological data, function, complex, Final, offer, parameter, approach, ENhance, greater, provided, outperform, yielding, 【제목키워드】 Bayesian, malaria,