Predictive Analytics on Covid-19 Prediction using ResNets

ABSTRACT

Corona virus acute disease, a life-threatening condition, emerged in 2019. In December 2019, the virus was discovered for the first time in Wuhan, China, and has since spread throughout the world. This paper proposes using Residual Neural Networks (ResNets) to predict COVID-19, where the input is collected from Internet of Things (IoT) network. Using a system designed to combat a newly emerging infection in its early stages, this paper tackles the problem. In addition to tracking confirmed and reported cases, the system also keeps tabs on cures and deaths daily. This was done so that all parties involved could see the devastation that the lethal virus would cause as soon as possible. Using RNN and GRU in an ensemble, the RMSE value has been computed for various cases such as infected, cured, and dead. The results of simulation shows that the proposed ResNets for classification is effective in predicting the covid-19 cases than the other existing deep learning models. © 2022 IEEE.