Open Journal of Statistics

Volume 13, Issue 2 (April 2023)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 1.45  Citations  

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

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DOI: 10.4236/ojs.2023.132012    182 Downloads   853 Views  
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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.

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Wang, L. (2023) Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models. Open Journal of Statistics, 13, 222-232. doi: 10.4236/ojs.2023.132012.

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