Analyzing the Spread of COVID-19 in India Through PageRank and Diffusion Techniques

Bhattacharya, S.; Siva Sathya, S.; Sharmiladevi, S.

From time immemorial, the world has faced and overcome several epidemics claiming millions of lives among which the corona virus disease-2019 (COVID-19) has become a global threat with nations trying to figure out the techniques to analyze, mitigate, and control the spread of the pandemic. Various mathematical and statistical models have been utilized to understand the spread of epidemics in the past. This paper tries to use the concepts and models used in social network analytics like page rank, diffusion, and cascading effects to understand the spread of COVID-19 in the Indian subcontinent. The identification of hot spot is done through the page rank algorithm, and the spread of the disease is explained through the process of diffusion and cascading effects. The concept has been implemented on the real-life dataset collected day-to-day from Kaggle using python to visualise the results and draw a conclusion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.