  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.
