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.

翻译：流行病学模型必须根据真实数据进行校准，以完成诸如生成前瞻性预测或运行假设情景等下游任务。对于随机模型而言，校准的含义会发生变化，因为此类模型的输出通常通过集成或分布来描述。集成中的每个成员通常映射到一个随机数种子（显式或隐式）。为了不仅找到与真实数据一致的输入参数设置，还找到随机种子，我们提出了一类基于高斯过程（GP）的替代模型，并结合了基于汤普森采样的优化策略。这种面向轨迹的优化（TOO）方法能够生成接近经验观测值的实际轨迹，而不是仅使平均模拟行为与真实数据匹配的一组参数设置。