Exploratory study of existing approaches for analyzing epidemics

The outbreak of epidemic diseases such as COVID-19, H1N1 swine flu, Ebola, and dengue has caused different communities to raise their apprehension over preventing and controlling the infectious diseases, as well as determining methods to diminish the disease propagation percentage. Epidemics are generally contiguous in which the number of cases increases at a very rapid rate. It often results in loss of lives as it affects the respiratory tract and lungs and even causes multiorgan failure. Hence, it is imperative to analyze the spread of any virus to make strategies for situational awareness and intervention. Researchers and medical practitioners have actively performed many studies to model the behavior of viruses with varied perspectives. These studies have guided in analyzing the pattern and speed of virus spread. This chapter presents an exploratory study on the existing approaches, such as classical epidemic approaches and Machine Learning approaches, useful for studying the outbreak patterns of epidemics. Besides, the chapter highlights the available epidemics datasets and describes the varied visualization charts that can help in understanding the patterns of virus spread.