A comprehensive statistical analysis of COVID-19 trends: Global and United States insights through ARIMA, regression, and spatial models

Abstract

The COVID-19 pandemic has driven the need for accurate data analysis and forecasting to support public health decision-making. This study applied autoregressive integrated moving average (ARIMA) models and ARIMA models with exogenous variables to predict short-term trends in confirmed COVID-19 cases across several regions, including the United States of America, Asia, Europe, and Africa. Model performance was compared between ARIMA and the automated model selection function, auto.arima, and anomaly detection was performed to investigate discrepancies between predicted and observed case numbers. Additionally, the study explored the relationship between vaccination rates and new case trends while also examining the influence of socioeconomic factors—such as gross domestic product per capita, human development index, and healthcare resources availability—on COVID-19 incidence across countries. The findings provide valuable insights into the effectiveness of predictive models and highlight the significant role of socioeconomic factors in the spread of the virus, thereby contributing to the development of more effective strategies for future epidemic prevention and control.

Keywords

Autoregressive integrated moving average model / COVID-19 / Public health / Socioeconomic factors / Time series forecasting / Vaccination rates

Funding

None.

Conflict of interest

The author declares no conflicts of interest.

References

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