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The COVID-19 pandemic has had a profound impact on global health and economies. The pandemic continues to spread and accurate forecasting of its spread is essential for the effective management of healthcare systems and the development of effective policies. The development of forecasting models for COVID-19 has become increasingly important as the pandemic continues to evolve. In this paper, we will summarize the Covid-19 pandemic in the United States state by state. And then, we utilize the temporal data of coronavirus spread from January 18, 2020 to January 29, 2023. Finally, we model the evolution of the COVID-19 outbreak and perform prediction using ARIMA and time series forecasting models on some selected states.
Rapidly spreading Covid-19 virus and its variants, especially in metropoli- tan areas around the world, became a major health public concern. The tendency of Covid-19 pandemic and statistical modelling represent an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate com- bining 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 ac- curacy improvement from 2020 to 2023. Most importantly, we provide a new advanced pathways which may serve as targets for developing new solutions and approaches.