Estimation of the parameters of an infectious disease model using neural networks

TL;DR

This paper introduces a neural network approach to estimate parameters in a nonlinear infectious disease model that accounts for mutual interference and vaccination effects, improving accuracy over traditional methods.

Contribution

The paper develops a novel neural network architecture based on cooperative and supportive networks for estimating epidemic model parameters.

Findings

The proposed neural network outperforms simple feed-forward neural networks.

It provides efficient estimation of epidemic spread rates.

The model incorporates vaccination effects as a function of infective individuals.

Abstract

In this paper, we propose a realistic mathematical model taking into account the mutual interference among the interacting populations. This model attempts to describe the control (vaccination) function as a function of the number of infective individuals, which is an improvement over the existing susceptible infective epidemic models. Regarding the growth of the epidemic as a nonlinear phenomenon we have developed a neural network architecture to estimate the vital parameters associated with this model. This architecture is based on a recently developed new class of neural networks known as co-operative and supportive neural networks. The application of this architecture to the present study involves preprocessing of the input data, and this renders an efficient estimation of the rate of spread of the epidemic. It is observed that the proposed new neural network outperforms a simple feed-forward neural network and polynomial regression.