Trajectory-oriented Optimization of Stochastic Epidemiological Models

Arindam Fadikar,Mickaël Binois,Nicholson Collier,Abby Stevens,Kok Ben Toh,Jonathan Ozik

Proceedings of the Winter Simulation Conference Winter Simulation Conference（2023）

Decision and Infrastructure Sciences | Acumes project-team | Department of Preventive Medicine

Cited 0|Views27

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.

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Key words

Stochastic Model,Epidemiological Models,Stochastic Epidemiological Model,Parameter Settings,Input Parameters,Gaussian Process,Ensemble Members,Actual Trajectory,Goal Of Finding,Epidemic,Symptom Severity,Covariance Matrix,Empirical Data,Alternative Models,Observational Data,Loss Of Generality,White Noise,Computer Simulations,Global Optimization,Stochastic Simulations,Dimensionality Of The Input Space,Bayesian Optimization,Input Space,Predictive Distribution,Simulated Activity,Discrete Set,Kriging,Input Parameter Values

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