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Comprehensive Review on Statistical Modeling Approach to Predict the COVID-19 Transmission

In: Proceedings of the International Conference on Emerging Trends in Business & Management (ICETBM 2023)

Author

Listed:
  • Vallaippan Raman

    (Coimbatore Institute of Technology, Department of Artificial Intelligence and Data Science)

  • Navin Aravinth

    (Coimbatore Institute of Technology, Department of Artificial Intelligence and Data Science)

  • Preetha Merlin Joy

    (Coimbatore Institute of Technology, Department of Artificial Intelligence and Data Science)

  • Kowsalya

    (Coimbatore Institute of Technology, Department of Artificial Intelligence and Data Science)

Abstract

This study aims to focus on the statistical model for forecasting the transmission of covid-19. The dynamics of the spreading nature can be determined by prediction models. Various prediction models are devised and/or used to know the disease dynamics and the existing ones based on statistical models are being developed for single or multiple countries. Many review articles commonly address the statistical models adopted, whereas the studies indicate effective models that address disease dynamics and forecast potential contagion scenarios viz. Data-driven techniques were created on different parameters. This work aims at collating the basic working philosophies of most cited COVID-19 dynamic prediction model reports by a systematic literature study. The review highlights the dynamic models strength and their weakness in predicting of SARS Covid-19. words.

Suggested Citation

  • Vallaippan Raman & Navin Aravinth & Preetha Merlin Joy & Kowsalya, 2023. "Comprehensive Review on Statistical Modeling Approach to Predict the COVID-19 Transmission," Advances in Economics, Business and Management Research, in: Sudarsan Jayasingh & Kirubaharan Boobalan & Thiruvenkadam Thiagarajan (ed.), Proceedings of the International Conference on Emerging Trends in Business & Management (ICETBM 2023), pages 112-129, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-162-3_11
    DOI: 10.2991/978-94-6463-162-3_11
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