Analysis and prediction of worldwide novel coronavirus (COVID-19) infections, using neural network-based techniques

Abstract

The novel coronavirus (COVID-19) outbreak has recently become a major public health concern around the world. It is commonly known that some of the world's most powerful countries, such as Iran and the United States, are suffering more than others from the effects of this horrific pandemic. It has spread throughout communities and has endangered the health of many people. Governments must take the necessary steps to stop the virus from spreading globally. The three most widely used backpropagation neural network (BPNN) techniques, i.e., Levenberg–Marquardt, Bayesian regularization (BR), and scaled conjugate gradient (SCG), are used to either predict the future or evaluate the current status of COVID-19 in this research. This study uses a real-time COVID-19 dataset from the Worldometer website, which contains 204 samples from 30 January to 15 April 2020. The 12 most important parameters are selected for study purposes, including country, total cases (TC), new cases (NC), total deaths (TD), new deaths (ND), total recoveries (TREV), active cases (AC), serious cases (SC), total tests (TT), death rate (DR), recovery rate (RR), and case rate (CR). Finally, countries are classified into three risk levels, i.e., high, medium, and low, based on the above parameters. In addition, some new countries are discovered at these levels.