  Coronavirus outbreak is one of the most challenging pandemics for the entire
human population of the planet Earth. Techniques such as the isolation of
infected persons and maintaining social distancing are the only preventive
measures against the epidemic COVID-19. The actual estimation of the number of
infected persons with limited data is an indeterminate problem faced by data
scientists. There are a large number of techniques in the existing literature,
including reproduction number, the case fatality rate, etc., for predicting the
duration of an epidemic and infectious population. This paper presents a case
study of different techniques for analysing, modeling, and representation of
data associated with an epidemic such as COVID-19. We further propose an
algorithm for estimating infection transmission states in a particular area.
This work also presents an algorithm for estimating end-time of an epidemic
from Susceptible Infectious and Recovered model. Finally, this paper presents
empirical and data analysis to study the impact of transmission probability,
rate of contact, infectious, and susceptible on the epidemic spread.
