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. This study investigates the potential of Gaussian Process emulation to overcome this obstacle. 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 trained three Gaussian Process surrogate models on three outcomes: outbreak probability, maximum incidence, and epidemic duration. These surrogate models enable the rapid prediction of outcomes at any point in the eigh

SUBMITTER: Langmuller AM 

PROVIDER: S-EPMC11623728 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature