Estimation of the parameters of an infectious disease model using neural networks
This work addresses parameter estimation for epidemic modeling, which is incremental as it builds on existing models by incorporating control functions and neural network improvements.
The authors tackled the problem of estimating parameters in an infectious disease model by developing a neural network architecture based on co-operative and supportive neural networks, which outperformed feed-forward neural networks and polynomial regression in efficiency.
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.