Predicting COVID-19 cases using bidirectional LSTM on multivariate time series
<p>To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional L...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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| _version_ | 1864513567477202944 |
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| author | Ahmed Ben Said (14158926) |
| author2 | Abdelkarim Erradi (13475740) Hussein Ahmed Aly (14151711) Abdelmonem Mohamed (14151714) |
| author2_role | author author author |
| author_facet | Ahmed Ben Said (14158926) Abdelkarim Erradi (13475740) Hussein Ahmed Aly (14151711) Abdelmonem Mohamed (14151714) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmed Ben Said (14158926) Abdelkarim Erradi (13475740) Hussein Ahmed Aly (14151711) Abdelmonem Mohamed (14151714) |
| dc.date.none.fl_str_mv | 2022-11-22T21:14:36Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11356-021-14286-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Predicting_COVID-19_cases_using_bidirectional_LSTM_on_multivariate_time_series/21597558 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Environmental engineering Environmental sciences Pollution and contamination Health, Toxicology and Mutagenesis Pollution Environmental Chemistry General Medicine |
| dc.title.none.fl_str_mv | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.</p><h2>Other Information</h2> <p> Published in: Environmental Science and Pollution Research<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s11356-021-14286-7" target="_blank">http://dx.doi.org/10.1007/s11356-021-14286-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_97b67a63744698c28efbffd2dd9a39b0 |
| identifier_str_mv | 10.1007/s11356-021-14286-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597558 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Predicting COVID-19 cases using bidirectional LSTM on multivariate time seriesAhmed Ben Said (14158926)Abdelkarim Erradi (13475740)Hussein Ahmed Aly (14151711)Abdelmonem Mohamed (14151714)EngineeringEnvironmental engineeringEnvironmental sciencesPollution and contaminationHealth, Toxicology and MutagenesisPollutionEnvironmental ChemistryGeneral Medicine<p>To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.</p><h2>Other Information</h2> <p> Published in: Environmental Science and Pollution Research<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s11356-021-14286-7" target="_blank">http://dx.doi.org/10.1007/s11356-021-14286-7</a></p>2022-11-22T21:14:36ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11356-021-14286-7https://figshare.com/articles/journal_contribution/Predicting_COVID-19_cases_using_bidirectional_LSTM_on_multivariate_time_series/21597558CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215975582022-11-22T21:14:36Z |
| spellingShingle | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series Ahmed Ben Said (14158926) Engineering Environmental engineering Environmental sciences Pollution and contamination Health, Toxicology and Mutagenesis Pollution Environmental Chemistry General Medicine |
| status_str | publishedVersion |
| title | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| title_full | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| title_fullStr | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| title_full_unstemmed | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| title_short | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| title_sort | Predicting COVID-19 cases using bidirectional LSTM on multivariate time series |
| topic | Engineering Environmental engineering Environmental sciences Pollution and contamination Health, Toxicology and Mutagenesis Pollution Environmental Chemistry General Medicine |