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

TL;DR

This paper analyzes and models COVID-19 spread in India using various techniques, proposing algorithms for transmission estimation and epidemic duration, supported by empirical data analysis of key transmission factors.

Contribution

It introduces new algorithms for estimating infection transmission states and epidemic end-time based on SIR models, with comprehensive data analysis.

Findings

Transmission probability significantly affects spread

Contact rate influences epidemic duration

Susceptible and infectious populations impact transmission dynamics

Abstract

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.