AUTHOR=Dhamodharavadhani S , Rathipriya R , Chatterjee Jyotir Moy 

TITLE=COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models

JOURNAL=Frontiers in Public Health

VOLUME=Volume 8 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2020.00441

DOI=10.3389/fpubh.2020.00441

ISSN=2296-2565

ABSTRACT=The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and 13 their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the 14 future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), 15 Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network 16 (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For 17 this purpose, we have used two datasets as D1 & D2. The performances of these models are evaluated 18 using Root Mean Square Error (RMSE) and 'R’, a correlation value between actual and predicted value. 19 To improve prediction accuracy, the new hybrid models have been constructed by combining SNN 20 models and the Nonlinear Autoregressive Neural Network (NAR-NN). This is to predict the future 21 error of the SNN models, which adds to the predicted value of these models for getting better MRP 22 value. The results showed that the PNN and RBFNN-based MRP model performed better than the 23 other models for COVID-19 datasets D2 and D1, respectively.