PDF(Pubmed)
Abstract：
The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.
摘要：
源自武汉的新型冠状病毒在世界范围内传播，中国导致了持续的COVID-19大流行。这种疾病是一种传染病，在印度通过有旅行史的人迅速传播到受影响的国家，和他们的接触测试呈阳性。所有州和联邦领土（UT）的数百万人受到影响，导致严重的呼吸道疾病和死亡。在本研究中,在COVID-19数据集上应用了两种无监督聚类算法，即k-means聚类和分层聚集聚类，以便根据3月份的大流行效应和疫苗接种计划对印度各州/UT进行分组，2020年6月初,2021年。该研究的目的是观察印度各州和UT对抗新型冠状病毒感染的困境，并监测其疫苗接种状况。这项研究将有助于政府和前线工作人员应对限制病毒在印度的传播。此外，研究结果将为未来有关印度COVID-19大流行的研究提供信息来源。
