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Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and
Saratu Yusuf Ilu1, Rajesh Prasad1
1Department of Computer Science, African University of Science and Technology, Abuja, Nigeria.
Heliyon
|February 13, 2023
Summary
A new ARIMAI model significantly improves COVID-19 trend prediction accuracy. This enhanced autoregressive integrated moving average (ARIMA) model offers a rapid and simple method for forecasting future pandemic diseases.
Area of Science:
- Epidemiology
- Data Science
- Time Series Analysis
Background:
- The COVID-19 pandemic has caused widespread respiratory illness globally.
- Accurate prediction of disease outbreaks is crucial for public health response.
Purpose of the Study:
- To enhance the autoregressive integrated moving average (ARIMA) time series model for improved COVID-19 forecasting.
- To develop a more accurate and efficient model for predicting pandemic trends.
Main Methods:
- Improved the ARIMA model by integrating statistical significance for feature selection.
- Incorporated k-means clustering for robust outlier detection.
- Validated the enhanced model (ARIMAI) using official World Health Organization COVID-19 data.
Main Results:
- The ARIMAI model achieved higher accuracy (97.75%) compared to the standard ARIMA model (93%).
- ARIMAI demonstrated a lower Residual Sum of Squares (RSS) score (0.279 vs. 0.659).
- The model outperformed other contemporary algorithms in predicting COVID-19 cases.
Conclusions:
- The ARIMAI model provides an efficient, rapid, and simple method for forecasting COVID-19 trends.
- This approach can be applied to predict future disease outbreaks during pandemics.
- The enhanced model offers a valuable tool for public health preparedness and response.
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