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

Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India