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Hybrid ARIMA-LSTM for COVID-19 forecasting: a comparative AI modeling study.

Al Mahmud¹, Syed Husni Noor Syed Hatim Noor¹, Kamarul Imran Musa²

¹School of Dental Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia.

Peerj. Computer Science

|September 24, 2025

Summary

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Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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This summary is machine-generated.

Hybrid ARIMA-LSTM models significantly improve pandemic forecasting accuracy compared to traditional ARIMA and deep learning LSTM models. This approach offers more reliable predictive analytics for epidemiology.

Area of Science:

Epidemiology
Data Science
Public Health

Background:

Pandemics pose significant global challenges requiring accurate forecasting.
Classical statistical models (ARIMA) struggle with nonlinear pandemic data.
Deep learning models (LSTM) show promise but require extensive resources.

Purpose of the Study:

To compare the forecasting performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models.
To evaluate model accuracy using COVID-19 data from Malaysia.
To determine the most effective modeling approach for pandemic trend prediction.

Main Methods:

Utilized autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.
Developed and tested a hybrid ARIMA-LSTM model.

Keywords:

COVID-19 prediction Hybrid ARIMA-LSTM model Machine learning in epidemiology Pandemic forecasting Predictive analytics for infectious diseases Time-series analysis

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Evaluated performance using metrics like MSE, MAE, MAPE, RMSE, RRMSE, NRMSE, and R².

Main Results:

ARIMA models showed poor performance in capturing pandemic trends.
LSTM models demonstrated improved forecasting accuracy over ARIMA.
The hybrid ARIMA-LSTM model consistently yielded the lowest error rates.

Conclusions:

Hybrid ARIMA-LSTM models offer superior pandemic forecasting accuracy.
Integrating statistical and deep learning methods enhances predictive analytics in epidemiology.
Hybrid models are recommended for reliable pandemic forecasting and resource allocation.