IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i4d10.1007_s40745-022-00404-w.html
   My bibliography  Save this article

Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm

Author

Listed:
  • S. Chakraborty

    (National Institute of Technology Karnataka)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-022-00404-w
    DOI: 10.1007/s40745-022-00404-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00404-w
    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/s40745-022-00404-w?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. Pai, Chintamani & Bhaskar, Ankush & Rawoot, Vaibhav, 2020. "Investigating the dynamics of COVID-19 pandemic in India under lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. 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.
    4. Aboma Temesgen & Abdisa Gurmesa & Yehenew Getchew, 2018. "Joint Modeling of Longitudinal CD4 Count and Time-to-Death of HIV/TB Co-infected Patients: A Case of Jimma University Specialized Hospital," Annals of Data Science, Springer, vol. 5(4), pages 659-678, December.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    17. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    18. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    19. Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    20. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.

    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:10:y:2023:i:4:d:10.1007_s40745-022-00404-w. 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.