A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness
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,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Health Care Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/hcs2.123 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850251902590124032 |
|---|---|
| author | Somit Jain Shobhit Agrawal Eshaan Mohapatra Kathiravan Srinivasan |
| author_facet | Somit Jain Shobhit Agrawal Eshaan Mohapatra Kathiravan Srinivasan |
| author_sort | Somit Jain |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7190cef3da814707bfe6999935e19f0e |
| institution | OA Journals |
| issn | 2771-1757 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Care Science |
| spelling | doaj-art-7190cef3da814707bfe6999935e19f0e2025-08-20T01:57:48ZengWileyHealth Care Science2771-17572024-12-013640942510.1002/hcs2.123A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparednessSomit Jain0Shobhit Agrawal1Eshaan Mohapatra2Kathiravan Srinivasan3School of Computer Science and Engineering, Vellore Institute of Technology Vellore IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology Vellore IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology Vellore IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology Vellore IndiaAbstract 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.https://doi.org/10.1002/hcs2.123COVID‐19time seriesARIMALSTMGRUprophet |
| spellingShingle | Somit Jain Shobhit Agrawal Eshaan Mohapatra Kathiravan Srinivasan A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness Health Care Science COVID‐19 time series ARIMA LSTM GRU prophet |
| title | A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness |
| title_full | A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness |
| title_fullStr | A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness |
| title_full_unstemmed | A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness |
| title_short | A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness |
| title_sort | novel ensemble arima lstm approach for evaluating covid 19 cases and future outbreak preparedness |
| topic | COVID‐19 time series ARIMA LSTM GRU prophet |
| url | https://doi.org/10.1002/hcs2.123 |
| work_keys_str_mv | AT somitjain anovelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT shobhitagrawal anovelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT eshaanmohapatra anovelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT kathiravansrinivasan anovelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT somitjain novelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT shobhitagrawal novelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT eshaanmohapatra novelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness AT kathiravansrinivasan novelensemblearimalstmapproachforevaluatingcovid19casesandfutureoutbreakpreparedness |