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Chyon, F.A., et al. (2022) Time Series Analysis and Predicting COVID-19 Affected Patients by ARIMA Model Using Machine Learning. Journal of Virological Methods, 301, 114433.
https://doi.org/10.1016/j.jviromet.2021.114433

has been cited by the following article:

• TITLE: Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

  AUTHORS: Lei Wang

  KEYWORDS: Dynamic Harmonic Regression with ARIMA Errors, COVID-19 Pandemic, Forecasting Models, Time Series Analysis, Weekly Seasonality

  JOURNAL NAME: Open Journal of Statistics, Vol.13 No.2, April 27, 2023

  ABSTRACT: Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.