What is it about?
This study presents a long-term retrospective analysis of COVID-19 case trends and develops a predictive model using the AutoRegressive Integrated Moving Average (ARIMA) approach. By examining historical COVID-19 data over an extended period, the research identifies patterns, fluctuations, and waves of infection. Different ARIMA model parameters were screened and evaluated to determine the most accurate forecasting model. The study ultimately demonstrates how statistical time-series modeling can be used to estimate future COVID-19 case numbers based on past observations.
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Why is it important?
COVID-19 has shown how rapidly infectious diseases can affect healthcare systems, economies, and communities worldwide. Reliable forecasting tools help decision-makers: Anticipate future outbreaks and infection waves. Allocate healthcare resources more efficiently. Support public health planning and preparedness. Improve early warning systems for emerging disease trends. Reduce uncertainty in epidemiological decision-making. By identifying the most effective ARIMA parameters, this research contributes to the development of practical forecasting frameworks that can be adapted to future public health emergencies.
Perspectives
Scientific Perspective The study highlights the value of classical statistical forecasting methods such as ARIMA for epidemiological surveillance. Despite the emergence of advanced machine learning approaches, ARIMA remains a robust, interpretable, and computationally efficient tool. Public Health Perspective Accurate forecasts can assist governments and healthcare authorities in planning interventions, managing hospital capacity, and responding proactively to changing disease dynamics. Future Research Perspective Future studies could: Compare ARIMA performance with machine learning and deep learning models. Incorporate vaccination rates, mobility data, and environmental factors. Develop hybrid forecasting frameworks for enhanced accuracy. Apply the methodology to other infectious diseases and public health challenges. Practical Perspective The parameter-screening framework presented in this study offers a reproducible approach that researchers and public health analysts can use when developing forecasting models for real-world applications.
Independent Researcher & Consultant Mostafa Essam Eissa
Read the Original
This page is a summary of: A Retrospective Long-Term Analysis of COVID-19 Cases Trends and a Predictive ARIMA Model Through Parameters Screening, Acta Natura et Scientia, June 2026, Prensip Publishing,
DOI: 10.61326/actanatsci.v7i1.404.
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