Study and impact analysis of COVID-19 pandemic clinical data on infection spreading

In December 2019, a severe pneumonialike disease has occurred in the city of Wuhan, Hubei Province in China. Within a very short period the infection spread across the whole world, but there was no previous medical history about this virus and how, where, and when the disease infected the human body and mutated in humans is still unknown. Subsequently, the coronavirus disease 2019 (COVID-19) outbreak was declared as the world pandemic on March 2020 by the World Health Organization because of its harmfulness and super spreading nature. Till now, there is no specific medications and clinical treatment available to avoid this pandemic COVID-19 outbreak. For this, it is essential to have a detailed study and analysis through the recent technologies. The recent trends such as artificial intelligence and machine learning (ML) based models can learn from past patient medication data and can suggest improvement accordingly by analyzing the data to control the spread. In the present scenario, the correct decision could be the appropriate precaution to stop spreading as well as controlling such a pandemic disease by proposing predictive ML that analyzes past data and conclude some useful information for future control of the spread of COVID-19 infections using minimum resources. The ML-based approach can be helpful to design different models to give a predictive solution for controlling infection and spreading and taking precaution toward the COVID-19 outbreak. In this chapter, we study the basic information of COVID-19 and its effectiveness worldwide. We also state the fundamental steps of ML, discuss the ML mechanism to study the pandemic for research and academic purposes, and study the data analytics of clinical data of India through a case study. As the data is a time series data, we analyze the data from March 1, 2020 to April 15, 2020; the decision tree approach of ML is discussed through a case study. Finally, the chapter is concluded with certain future scope of work in this area of research.