Forecasting of Novel Corona Virus Disease (Covid‐19) Using LSTM and XG Boosting Algorithms

Abstract

The viruses are called as enteric viruses developed using ingestion termed as fecal oral transmission and is relicated using intestinal tract. Enteric viruses are genus Enterovirus phrased as Caliciviridae, Picoornaviridae, Coronaviride, Astroviridae, Orthoreovirus, genera Rotavirus, and Reoviridae phrase as Adenovirida and Reoviridae. Coronaviruses belong to the Coronaviridae family. It belongs to one of the Ribo-Nucleic Acid (RNA) families of the order Nidovirales, the others are pathegens of birds and insects of Arteriviridae and the Roniviridae families. The coronaviruses consists of single stranded RNA genome of 30kb in length in size. An epidemic of novel corona virus called as SARS-CoV-2 irritates the COVID- 19 disease is reported recently. It is enveloped, plus stranded RNA viruses with extra ordinarily large genomes and helical nucleocapsids. During the pandemic situations, it is necessary to predict Covid cases in advance to take the preventive measures and thus saving the human life and other living beings. To predict the count of Covid-19 in advance and to improve the accuracy, this chapter proposes Machine and Deep learning algorithms such as Long Short Term Memory (LSTM), eXtreme Gradient Boost (XG Boost) algorithms and polynomial regression for forecasting. The real time dataset is taken from Kaggle which contains around 36,000 samples. The sample is taken from around 187 countries from the world and the dataset contains the details which included from the month of January to May, 2020. The algorithm is tested using test dataset and the performance is evaluated through the performance metrics.