Skip to main content
SpringerLink
Log in

Machine Learning Techniques for Covid-19 Pandemic Updates for Analysis, Visualization, and Prediction System

  • Conference paper
  • First Online:
Machine Learning and Big Data Analytics (ICMLBDA 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 401))

Included in the following conference series:

  • 446 Accesses

Abstract

The spread of Covid-19 sickness 2019 (coronavirus) has turned into a worldwide danger, and the World Well-Being Association (WHO) pronounced coronavirus a worldwide pandemic on 28/10/2021. As of Oct 30, 2021, there were 244,897,472 affirmed cases and 4,970,435 passings from coronavirus around the world. The coronavirus pandemic has been incredibly influencing individuals’ lives and the world’s economy. The proposed forecast models have dissected, pictured, and anticipated the coronavirus cases internationally and country-wise. Information is assembled from various information sources—a few bona fide government sites. Time series estimation methods including AI models like straight relapse, backing vector relapse (SVM), polynomial relapse (PR), and Bayesian edge relapse strategies are conveyed to concentrate on the plausible climb in cases and soon.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu Y Li J, Guo K, Viedma EH, Lee H, Liu J, Zhong N, Gomes LFAM, Filip FG, Fang SC, Özdemir, Gu Z, Xia S, Shi B, Zhou XN, Shi Y, Liu J (2020) What are the underlying transmission patterns of COVID-19 outbreak?—an age-specific social contact characterization. EClinicalMedicine 22:10035.

    Article  Google Scholar 

  2. Temesgen A, Gurmesa A, Getchew Y (2018) Joint modeling of longitudinal cd4 count and timeto-death of hiv/tb co-infected patients: a case of Jimma university specialized hospital. Ann Data Sci 5(4):659

    Article  Google Scholar 

  3. Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill/Irwin, Englewood Clifs

    Google Scholar 

  4. MS et al (2020) Culture vs policy: more global collaboration to effectively combat COVID-19. Innovation 1(2):100023

    Google Scholar 

  5. https://www.worldometers.info/coronavirus/worldwide-graphs/

  6. https://www.worldometers.info/coronavirus/coronavirus-cases/

  7. Vomlel J, Kruzık H, Tuma P, Precek J, Hutyra M (2012) Machine learning methods for mortality prediction in patients with st elevation myocardial infarction. Proc WUPES 17(1):204

    Google Scholar 

  8. Kumar P, Kalita H, Patairiya S, Sharma YD, Nanda C, Rani M, Rahmani J, Bhagavathula AS (2020) Forecasting the dynamics of COVID-19 pandemic in top 15 countries in April 2020: Arima model with machine learning approach. medRxiv

    Google Scholar 

  9. Tuli S, Tuli S, Tuli R, Gill SS (2020) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things 11:100222

    Article  Google Scholar 

  10. Petropoulos F, Makridakis S (2020) Forecasting the novel coronavirus COVID-19. PLoS ONE 15(3):e0231236

    Article  Google Scholar 

  11. Wu JT, Leung K, Leung GM (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395(10225):689

    Article  Google Scholar 

  12. Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, Liu P, Cao X, Gao Z, Mai Z et al (2020) Modifed SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 12(3):165

    Article  Google Scholar 

  13. Bhatnagar MR (2020) Covid-19: mathematical modeling and predictions, submitted to ARXIV. http://web.iitd.ac.in/~manav/COVID.pdf

  14. Zhang X, Ma R, Wang L (2020) Predicting turning point, duration and attack rate of COVID-19 outbreaks in major western countries. Chaos Solitons Fractals 135:109829

    Article  Google Scholar 

  15. Maier BF, Brockmann D (2020) Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Science 368(6492):742

    Article  MathSciNet  MATH  Google Scholar 

  16. Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, Wang D, Chen G, Zhang J, Peng H et al (2020) Propagation analysis and prediction of the COVID-19. Infect Dis Model 5:282

    Google Scholar 

  17. Carrieri, V., Lagravinese, R., & Resce, G. (2021). Predicting vaccine hesitancy from area-level indicators: A machine learning approach. medRxiv

    Google Scholar 

  18. Ritonga, M., Al Ihsan, M. A., Anjar, A., & Rambe, F. H. (2021, February). Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 1088, No. 1, p. 012045). IOP Publishing.

    Google Scholar 

  19. Analysis, visualization and prediction of COVID-19 pandemic spread using machine learning By, Sen, S.; Thejas, B. K.; Pranitha, B. L.; Amrita, I.

    Google Scholar 

  20. https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

Download references

Author information

Authors and Affiliations

  1. Raghu Engineering College, Department of Computer Science and Engineering, Visakhapatnam, Andhra Pradesh, India

    D. Radha, P. Ratna Kumari & M. Dhanalakshmi

Authors
  1. D. Radha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Ratna Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Dhanalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science & Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Rajiv Misra

  2. Cardiff University, Cardiff, UK

    Rana Omer

  3. Dept. of EE Engineering, University of London, London, UK

    Muttukrishnan Rajarajan

  4. Dept. of ECE, National University of Singapore, Singapore, Singapore

    Bharadwaj Veeravalli

  5. Dept. of Computer Science, Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

    Nishtha Kesswani

  6. Dept. of CSE, Indian Institute of Information Technology, Kota, Jawahar Lal Nehru Marg, Rajasthan, India

    Priyanka Mishra

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Radha, D., Ratna Kumari, P., Dhanalakshmi, M. (2023). Machine Learning Techniques for Covid-19 Pandemic Updates for Analysis, Visualization, and Prediction System. In: Misra, R., Omer, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Mishra, P. (eds) Machine Learning and Big Data Analytics. ICMLBDA 2022. Springer Proceedings in Mathematics & Statistics, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-031-15175-0_43

Download citation

Publish with us

Policies and ethics

Search

Navigation