Changing Clusters of Indian States with respect to number of Cases of COVID-19 using incrementalKMN Method
This provides a domain-specific analysis for public health officials in India to monitor spatial variations in COVID-19 cases, but it is incremental as it applies an existing clustering method to new data.
The paper tackles the problem of tracking COVID-19 risk clusters among Indian states over time, using the incrementalKMN method to classify states into five risk categories and analyze their movements from January to June 2020.
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))