ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis

Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quantify parameter uncertainty. We propose a method for calibratin...

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Principais autores:	Thomas Bayley, Tony Ward, Fabian Sturman, Akashaditya Das, Luca Imeneo, Cliff Kerr, Christophe Fraser, Simon Maskell, Jasmina Panovska-Griffiths
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2026-03-01
Series:	Epidemics
Assuntos:	Machine-learning algorithms Outbreak control Infectious disease modelling Agent-based models
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S1755436525000696
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