Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

Abstract

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was −0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

Keywords

• Reliability function
• Maximum likelihood estimation
• Artificial neural network
• Failure rate function