On Predicting COVID-19 Fatality Ratio Based on Regression Using Machine Learning Model

Abstract

The world has been in the grips of the Coronavirus Disease-19 (COVID-19) pandemic for almost two years since December 2019. Since then the virus has infected over a hundred and fifty million and has resulted in over three million deaths. However, fatality rates have been observed to be drastically different in different countries. One reason could be the emergence of variants with differing virulence. Other factors such as demographic, health parameters, nutrition levels, and health care quality and access as well as environmental factors may contribute to the difference in fatality rates. To investigate the level of contributions of these different factors on mortality rates, we proposed a regression model using deep neural network to analyze health, nutrition, demographic, and environmental parameters during the COVID-19 lockdown period. We have used this model as it can address multivariate prediction problems with higher accuracy. The model has proved very useful in making associations and predictions with low Mean Absolute Error (MAE).