AccScience Publishing / MI / Online First / DOI: 10.36922/MI025040007
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ORIGINAL RESEARCH ARTICLE

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

Zhihao Lei1,2*
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1 School of Mathematics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
2 Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island, United States of America
MI, 025040007 https://doi.org/10.36922/MI025040007
Received: 22 January 2025 | Revised: 9 April 2025 | Accepted: 12 May 2025 | Published online: 19 June 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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.
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Microbes & Immunity, Electronic ISSN: 3029-2883 Print ISSN: 3041-0886, Published by AccScience Publishing
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