ArticleIEEE transactions on computational social systems2021
Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India.
Article in IEEE transactions on computational social systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Coronavirus outbreak is one of the challenging pandemics for the entire human population on Earth. Techniques, such as the isolation of infected people and maintaining social distancing, are the only preventive measures against the pandemic. The actual estimation of the number of infected peoples with limited data is an indeterminate problem faced by data scientists. There are several techniques in the existing literature, including reproduction number and case fatality rate, for predicting the duration of a pandemic and infectious population. This article presents a case study of different techniques for analyzing, modeling, and representing the data associated with a pandemic 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 a pandemic from the susceptible infectious and recovered model. Finally, this article presents the empirical and data analysis to study the impact of transmission probability, rate of contact, infectious, and susceptible population on the pandemic spread.
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