Year: 2023
Type: conference-paper
Abstract: Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration changes in case of a stochastic model since output from such a model is generally described via an ensemble or a distribution. Each member of the ensemble is usually mapped to a random number seed (explicitly or implicitly). With the goal of finding not only the input parameter settings but also the random seeds that are consistent with the ground truth, we propose a class of Gaussian process (GP) surrogates along with an optimization strategy based on Thompson sampling. This Trajectory Oriented Optimization (TOO) approach produces actual trajectories close to the empirical observations instead of a set of parameter settings where only the mean simulation behavior matches with the ground truth.
Source: Winter Simulation Conference
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G102163711, G107546177, G1313983767, G1664757510, G2287611682, G262962599, PIPP Phase I: Robust Epidemic Surveillance and Modeling (RESUME), G3075337988, G3361544315, Next-Generation Phylodynamics-targeted Partner Service Models for Combined HIV Prevention, G498139845, G513797252, G5607048534, G6848031779, G6918803902, G9000174660, G969889393, G993149817
OpenAlex ID: W4391409165