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Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis

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  • Sanjay Kumar

    (Central University of Rajasthan)

Abstract

It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for treating massive volume of infectious disease data set. In the current study, we apply cluster analysis, one of the data mining techniques to classify real groups of infectious disease “novel corona virus disease (COVID-19)” data set of different states and union territories (UTs) in India according to their high similarity to each other. The results obtained permit us to have a sense of clusters of affected Indian states and UTs. The main objective of clustering in this study is to optimize monitoring techniques in affected states and UTs in India which will be very valuable to the government, doctors, the police and others involved in understanding seriousness of the spread of novel coronavirus (COVID-19) to improve government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. to reduce number of infected and deceased persons.

Suggested Citation

  • Sanjay Kumar, 2020. "Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis," Annals of Data Science, Springer, vol. 7(3), pages 417-425, September.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00289-7
    DOI: 10.1007/s40745-020-00289-7
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    References listed on IDEAS

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    1. Yong Shi & Zhiguang Shan & Jianping Li & Yufei Fang, 2017. "How China Deals with Big Data," Annals of Data Science, Springer, vol. 4(4), pages 433-440, December.
    2. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

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    Cited by:

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    2. Tousifur Rahman & Partha Jyoti Hazarika & M. Masoom Ali & Manash Pratim Barman, 2022. "Three-Inflated Poisson Distribution and its Application in Suicide Cases of India During Covid-19 Pandemic," Annals of Data Science, Springer, vol. 9(5), pages 1103-1127, October.
    3. Anurag Pathak & Manoj Kumar & Sanjay Kumar Singh & Umesh Singh, 2022. "Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data," Annals of Data Science, Springer, vol. 9(1), pages 101-119, February.
    4. Aman Khakharia & Vruddhi Shah & Sankalp Jain & Jash Shah & Amanshu Tiwari & Prathamesh Daphal & Mahesh Warang & Ninad Mehendale, 2021. "Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 1-19, March.
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    6. Vrushabh Gada & Madhura Shegaonkar & Madhura Inamdar & Sharath Dinesh & Darshan Sapariya & Vedant Konde & Mahesh Warang & Ninad Mehendale, 2022. "Data Analysis of COVID-19 Hospital Records Using Contextual Patient Classification System," Annals of Data Science, Springer, vol. 9(5), pages 945-965, October.
    7. Hanem Mohamed & Salwa A. Mousa & Amina E. Abo-Hussien & Magda M. Ismail, 2022. "Estimation of the Daily Recovery Cases in Egypt for COVID-19 Using Power Odd Generalized Exponential Lomax Distribution," Annals of Data Science, Springer, vol. 9(1), pages 71-99, February.
    8. S. Chakraborty, 2023. "Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm," Annals of Data Science, Springer, vol. 10(4), pages 967-989, August.
    9. Souvik Banerjee & Triparna Bose & Vijay M. Patil & Atanu Bhattacharjee & Kumar Prabhash, 2023. "Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial," Annals of Data Science, Springer, vol. 10(1), pages 209-223, February.
    10. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    11. Cerqueti, Roy & Ficcadenti, Valerio, 2022. "Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    12. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    13. Muhammad Ahsan-ul-Haq & Mukhtar Ahmed & Javeria Zafar & Pedro Luiz Ramos, 2022. "Modeling of COVID-19 Cases in Pakistan Using Lifetime Probability Distributions," Annals of Data Science, Springer, vol. 9(1), pages 141-152, February.
    14. Elphas Okango & Henry Mwambi, 2022. "Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response," Annals of Data Science, Springer, vol. 9(1), pages 175-186, February.
    15. Vali Borimnejad & Sahar Dehyouri, 2022. "Content Analysis of the Economic Problems of Covid-19 Disease on Businesses: A Case Study of Tehran Province, Iran," Annals of Data Science, Springer, vol. 9(5), pages 1069-1083, October.
    16. Weijia Xu & Aihua Li & Lu Wei, 2022. "The Impact of COVID-19 on China’s Capital Market and Major Industry Sectors," Annals of Data Science, Springer, vol. 9(5), pages 983-1007, October.
    17. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    18. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.

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