On the calibration of compartmental epidemiological models

TL;DR

This paper reviews calibration strategies for epidemiological compartmental models, comparing optimization and reinforcement learning methods to improve disease forecasting accuracy and inform public health policies.

Contribution

It provides a comprehensive overview of calibration techniques, discusses their advantages and limitations, and highlights practical insights from experiments, advancing the field of disease modeling.

Findings

Optimization iteratively matches model output to data.

Reinforcement learning learns parameters via trial and error.

Calibration methods can enhance disease prediction accuracy.

Abstract

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.