IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v7y2020i3d10.1007_s40745-020-00289-7.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00289-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-020-00289-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Asima Saleem, 2022. "Action for Action: Mad COVID-19, Falling Markets and Rising Volatility of SAARC Region," Annals of Data Science, Springer, vol. 9(1), pages 33-54, February.
    2. Desmond Chekwube Bartholomew & Chrysogonus Chinagorom Nwaigwe & Ukamaka Cynthia Orumie & Godwin Onyeka Nwafor, 2024. "Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series," Annals of Data Science, Springer, vol. 11(5), pages 1609-1634, October.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Anjan Mukherjee & Abhik Mukherjee, 2022. "Interval-Valued Intuitionistic Fuzzy Soft Rough Approximation Operators and Their Applications in Decision Making Problem," Annals of Data Science, Springer, vol. 9(3), pages 611-625, June.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Md. Rezaul Karim & Sefat-E-Barket, 2024. "Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh," Annals of Data Science, Springer, vol. 11(5), pages 1581-1607, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
    2. Emmanuel Afuecheta & Chigozie Utazi & Edmore Ranganai & Chibuzor Nnanatu, 2023. "An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies," Annals of Data Science, Springer, vol. 10(2), pages 251-290, April.
    3. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    4. Braznev Sarkar & Malay Bhattacharyya, 2021. "Spectral Algorithms for Streaming Graph Analysis: A Survey," Annals of Data Science, Springer, vol. 8(4), pages 667-681, December.
    5. Atanu Bhattacharjee, 2020. "Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials," Annals of Data Science, Springer, vol. 7(3), pages 447-460, September.
    6. Guillaume Wunsch & Federica Russo & Michel Mouchart & Renzo Orsi, 2020. "Time and Causality in the Social Sciences," Working Papers wp1155, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2021. "Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News," Annals of Data Science, Springer, vol. 8(3), pages 517-558, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00289-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.