Time Series based Models for Corona Data Analytics

There is a great challenge to deal with prediction of an epidemic or pandemic in the future through artificial intelligence or state-of-art technology. This is evident in the case of pandemic happened from January 2020 which is a result of corona virus. In early stages of covid-19 caused by corona virus, the symptoms are not severe and mostly cured through self-medication. In this situation, estimating the real spread based on the reports from various hospitals might be misleading. There might be lot of variation in the reports based on different types of measurements performed, and the tests conducted on only the symptomatic patients. In spite of all these constraints, a huge amount of covid-19 related data is published since 3 years and also updated on a daily basis. This serves as a motivation to consider various mathematical models to predict the course of change in an epidemic and result in effective control strategies. The challenge is to predict the peak and end of the epidemic together with its evolution through available incomplete data and intrinsic complexity. In this paper, time series models are proposed to analyze corona spread data and analyzing its impact based on gender, age and geographical location. The proposed algorithm leverages machine learning models to predict number of corona cases in the future. An early detection of spread of corona would help in stopping community transmission and this serves a major motivation for this research. ARIMA model and Recurrent Neural Networks (RNN) based LSTM model perform way better than the machine learning models based on regression and decision trees.