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

Vallaippan Raman (), Navin Aravinth, Preetha Merlin Joy and Kowsalya
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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

A chapter in Proceedings of the International Conference on Emerging Trends in Business & Management (ICETBM 2023), 2023, pp 112-129 from Springer

Abstract: 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.

Keywords: Forecasting; COVID-19; Statistical Models; Machine Learning Methods (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-162-3_11

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DOI: 10.2991/978-94-6463-162-3_11

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