Gaussian process emulation for exploring complex infectious disease models
Fig 4
Summary of model outcomes related to outbreak probability.
(A) First-order sensitivity index estimates for the first case timing parameter across varying average infectivity and average mobility values. For each parameter combination, we evaluated a total of 294,912 points. We varied all other parameters across their full ranges (Table 1). The first-order effect measures the influence of a single parameter on the model output (outbreak probability). Yellow stars mark parameter combinations associated with specific model outcomes shown in (B). (B) Predicted outbreak probabilities using the Gaussian Process surrogate model with varying seasonality strength and first case timing values. Panels represent different average infectivities. All other parameters were fixed at default values (Table 1), except for average mobility, which was set to 1.5. (C) Outbreak probabilities inferred from the individual-based model, with varying seasonality strength and first case timing values. Panels represent different average infectivities. As in (B), the remaining parameters were fixed at default values (Table 1), except for average mobility which was set to 1.5. (B) and (C) thus represent model outcomes for the same model parameters, but conducted with the Gaussian Process surrogate model (B) versus the original individual-based model (C), allowing a direct comparison between the two.