Epidemiological compartmental models are useful for understanding infectious disease propagation and directing public health policy decisions. Calibration of these models is an important step in offering accurate forecasts of disease dynamics and the effectiveness of interventions. In this study, we present an overview of calibrating strategies that can be employed, including several optimization methods and reinforcement learning (RL). We discuss the benefits and drawbacks of these methods and highlight relevant practical conclusions from our experiments. Optimization methods iteratively adjust the parameters of the model until the model output matches the available data, whereas RL uses trial and error to learn the optimal set of parameters by maximizing a reward signal. Finally, we discuss how the calibration of parameters of epidemiological compartmental models is an emerging field that has the potential to improve the accuracy of disease modeling and public health decision-making. Further research is needed to validate the effectiveness and scalability of these approaches in different epidemiological contexts. All codes and resources are available on \url{https://github.com/Nikunj-Gupta/On-the-Calibration-of-Compartmental-Epidemiological-Models}. We hope this work can facilitate related research.

流行病学分区模型的参数校准是一个新兴领域，有潜力提高疾病建模和公共卫生决策的准确性。本文概述了可采用的校准策略，包括多种优化方法和强化学习，并讨论了这些方法的优点和缺点以及实验结果的实际结论。

关于隔室流行病学模型的校准