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Original Article | Open Access

A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness

Somit Jain, Shobhit Agrawal, Eshaan Mohapatra, Kathiravan Srinivasan ()
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Somit Jain, Shobhit Agrawal, and Eshaan Mohapatra contributed equally to this study.

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A novel ensemble model that combines auto‐regressive integrated moving average (ARIMA) and long short‐term memory (LSTM) models is proposed to improve the forecasting accuracy of COVID‐19 cases. The proposed ensemble model achieves superior performance compared to individual ARIMA, LSTM, and other baseline forecasting models. This approach has the potential to enhance preparedness for future outbreaks by providing more accurate forecasts of COVID‐19 cases.

Abstract

Background

The global impact of the highly contagious COVID‐19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID‐19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.

Methods

The proposed approach combines auto‐regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short‐term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA‐artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID‐19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet.

Results

The hybrid ARIMA‐LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%.

Conclusions

The proposed ARIMA‐LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA‐ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.

Keywords

COVID‐19time seriesARIMALSTMGRUprophetANNproposed model

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Health Care Science
Volume 3 Issue 6,
December 2024
Pages 409-425
DOI: 10.1002/hcs2.123
Cite this article:
Jain S, Agrawal S, Mohapatra E, et al. A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness. Health Care Science, 2024, 3(6): 409-425. https://doi.org/10.1002/hcs2.123
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