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Related Experiment Videos

Enhancing COVID-19 Forecasting Accuracy in Malaysia Using a Hybrid ARIMA-LSTM Model With Exogenous Variables: A

Al Mahmud1,2, Kamarul Imran Musa3, Firdaus Mohamad Hamzah4

  • 1Department of Statistics Shahjalal University of Science & Technology Sylhet Bangladesh.

Health Science Reports
|June 24, 2026
PubMed
Summary

Related Concept Videos

Steps in Outbreak Investigation01:18

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.

This study introduces a hybrid ARIMA-LSTM model for accurate COVID-19 case forecasting. Integrating external factors like weather and vaccination rates significantly improved prediction accuracy compared to traditional methods.

Area of Science:

  • Epidemiology
  • Data Science
  • Public Health

Background:

  • Accurate COVID-19 forecasting is crucial for public health planning.
  • Traditional models struggle with linear dynamics, nonlinear patterns, and external drivers.
  • A novel hybrid framework is proposed to address these limitations.

Purpose of the Study:

  • To develop an enhanced COVID-19 forecasting model.
  • To integrate linear and nonlinear modeling with exogenous variables.
  • To improve the accuracy and reliability of epidemic predictions.

Main Methods:

  • A hybrid ARIMA-LSTM framework was developed.
  • Exogenous variables (temperature, rainfall, vaccination rate, population density) were incorporated.
  • Performance was evaluated against baseline models using RMSE, MAE, MAPE, and R².
Keywords:
ARIMACOVID‐19LSTMmachine learningtime series forecasting

Related Experiment Videos

Main Results:

  • The hybrid ARIMAX-LSTM model demonstrated superior predictive accuracy.
  • Prediction error was reduced by approximately 49% compared to baseline models.
  • The model showed robust performance during epidemic transitions, with significantly lower error rates.

Conclusions:

  • Integrating exogenous variables into both linear and nonlinear components enhances forecasting.
  • The hybrid ARIMAX-LSTM model offers a reliable tool for epidemic prediction.
  • This approach can be extended to other infectious diseases and time-series forecasting.