Cross-entropy method in application to SIRC model

TL;DR

This paper explores applying the Cross-Entropy optimization method to enhance parameter estimation in the SIRC epidemiological model, aiming for faster and more accurate results using weighted sampling techniques.

Contribution

It demonstrates how the Cross-Entropy method can be adapted for optimizing epidemiological models, specifically the SIRC model, with a focus on improving efficiency and accuracy.

Findings

Improved optimization of the SIRC model parameters.

Enhanced accuracy using weighted sampling in CE method.

Potential for faster convergence in epidemiological modeling.

Abstract

The study considers the usage of a probabilistic optimization method called Cross-Entropy (CE). This is the version of the Monte Carlo method created by Reuven Rubinstein (1997). It was developed in the context of determining rare events. Here we will present the way in which the CE method can be used for problems of optimization of epidemiological models, and more specifically the optimization of the SIRC (Susceptible - Infectious - Recovered - Cross-immune) model based on the functions supervising the care of specific groups in the model. With the help of weighted sampling, an attempt was made to find the fastest and most accurate version of the algorithm.