Gaussian process emulation for exploring complex infectious disease models.

ABSTRACT: Epidemiological models that aim for a high degree of biological realism by simulating every individual in a population are unavoidably complex, with many free parameters, which makes systematic explorations of their dynamics computationally challenging. In this study, we demonstrate how Gaussian Process emulation can overcome this challenge. To simulate disease dynamics, we developed an abstract individual-based model that is loosely inspired by dengue, incorporating some key features shaping dengue epidemics such as social structure, human movement, and seasonality. We focused on three epidemiological metrics derived from the individual-based model outcomes - outbreak probability, maximum incidence, and epidemic duration - and trained three Gaussian Process surrogate models to approximate

SUBMITTER: Langmuller AM 

PROVIDER: S-EPMC12774377 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature