Changing Clusters of Indian States with respect to number of Cases of COVID-19 using incrementalKMN Method

TL;DR

This paper introduces a new clustering method, incrementalKMN, to analyze the changing COVID-19 risk clusters among Indian states from January to June 2020, revealing spatial and temporal variations.

Contribution

The study applies a novel incrementalKMN clustering technique to track COVID-19 risk category shifts across Indian states over time.

Findings

States moved between risk clusters over time

Spatial variation in COVID-19 growth rates identified

New clustering method effectively captures temporal changes

Abstract

The novel Coronavirus (COVID-19) incidence in India is currently experiencing exponential rise but with apparent spatial variation in growth rate and doubling time rate. We classify the states into five clusters with low to the high-risk category and study how the different states moved from one cluster to the other since the onset of the first case on $30^{th}$ January 2020 till the end of unlock 1 that is $30^{th}$ June 2020. We have implemented a new clustering technique called the incrementalKMN (Prasad, R. K., Sarmah, R., Chakraborty, S.(2019))