Prediction of influenza a cases in tropical climate country using deep learning model

Influenza remains a significant public health concern particularly in tropical climate countries. Accurate prediction of influenza cases is crucial for effective resource allocation and public health planning. This study aimed to design and evaluate deep learning models for the monthly prediction of...

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Main Authors: Abd. Rahim, Muhamad Sharifuddin, Yakub, Fitri, Omar, Mas, Abd. Ghani, Rasli, Inge Dhamanti, Inge Dhamanti, Sivakumar, Soubraylu
Format: Conference or Workshop Item
Published: 2023
Subjects:
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author Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Inge Dhamanti, Inge Dhamanti
Sivakumar, Soubraylu
author_facet Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Inge Dhamanti, Inge Dhamanti
Sivakumar, Soubraylu
author_sort Abd. Rahim, Muhamad Sharifuddin
collection ePrints
description Influenza remains a significant public health concern particularly in tropical climate countries. Accurate prediction of influenza cases is crucial for effective resource allocation and public health planning. This study aimed to design and evaluate deep learning models for the monthly prediction of influenza A cases in a tropical climate country specifically Malaysia. The models considered both univariate and multivariate input configurations incorporating temperature and humidity variables. The study utilized a dataset spanning from January 2006 to December 2019 with training data from January 2006 to December 2016 and test data until December 2019. Various deep learning models such as RNN, LSTM, complex LSTM, GRU, Transformer and Informer were implemented and evaluated. The results demonstrated that the RNN variants specifically LSTM and GRU consistently outperformed the transformer and informer models for both univariate and multivariate prediction. The shorter input sequences (2 months) yielded better performance compared to longer sequences (12 months) with mean square error (MSE) of 0.0069, capturing more relevant temporal patterns and dependencies. The deep learning models produce a better ability to capture complex relationships and temporal patterns in the data. The findings highlight the effectiveness of the RNN variants and the impact of input configurations and sequence lengths on predictive performance. These findings provide valuable insights that can support public health planning and decision-making processes. Dataset can be obtained from the https://shorturl.at/joqxA.
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spelling utm.eprints-1077602024-10-02T07:21:27Z http://eprints.utm.my/107760/ Prediction of influenza a cases in tropical climate country using deep learning model Abd. Rahim, Muhamad Sharifuddin Yakub, Fitri Omar, Mas Abd. Ghani, Rasli Inge Dhamanti, Inge Dhamanti Sivakumar, Soubraylu T Technology (General) Influenza remains a significant public health concern particularly in tropical climate countries. Accurate prediction of influenza cases is crucial for effective resource allocation and public health planning. This study aimed to design and evaluate deep learning models for the monthly prediction of influenza A cases in a tropical climate country specifically Malaysia. The models considered both univariate and multivariate input configurations incorporating temperature and humidity variables. The study utilized a dataset spanning from January 2006 to December 2019 with training data from January 2006 to December 2016 and test data until December 2019. Various deep learning models such as RNN, LSTM, complex LSTM, GRU, Transformer and Informer were implemented and evaluated. The results demonstrated that the RNN variants specifically LSTM and GRU consistently outperformed the transformer and informer models for both univariate and multivariate prediction. The shorter input sequences (2 months) yielded better performance compared to longer sequences (12 months) with mean square error (MSE) of 0.0069, capturing more relevant temporal patterns and dependencies. The deep learning models produce a better ability to capture complex relationships and temporal patterns in the data. The findings highlight the effectiveness of the RNN variants and the impact of input configurations and sequence lengths on predictive performance. These findings provide valuable insights that can support public health planning and decision-making processes. Dataset can be obtained from the https://shorturl.at/joqxA. 2023 Conference or Workshop Item PeerReviewed Abd. Rahim, Muhamad Sharifuddin and Yakub, Fitri and Omar, Mas and Abd. Ghani, Rasli and Inge Dhamanti, Inge Dhamanti and Sivakumar, Soubraylu (2023) Prediction of influenza a cases in tropical climate country using deep learning model. In: 2023 IEEE 2nd National Biomedical Engineering Conference (NBEC), 5 September 2023-7 September 2023, Melaka, Malaysia. http://dx.doi.org/10.1109/NBEC58134.2023.10352612
spellingShingle T Technology (General)
Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Inge Dhamanti, Inge Dhamanti
Sivakumar, Soubraylu
Prediction of influenza a cases in tropical climate country using deep learning model
title Prediction of influenza a cases in tropical climate country using deep learning model
title_full Prediction of influenza a cases in tropical climate country using deep learning model
title_fullStr Prediction of influenza a cases in tropical climate country using deep learning model
title_full_unstemmed Prediction of influenza a cases in tropical climate country using deep learning model
title_short Prediction of influenza a cases in tropical climate country using deep learning model
title_sort prediction of influenza a cases in tropical climate country using deep learning model
topic T Technology (General)
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AT omarmas predictionofinfluenzaacasesintropicalclimatecountryusingdeeplearningmodel
AT abdghanirasli predictionofinfluenzaacasesintropicalclimatecountryusingdeeplearningmodel
AT ingedhamantiingedhamanti predictionofinfluenzaacasesintropicalclimatecountryusingdeeplearningmodel
AT sivakumarsoubraylu predictionofinfluenzaacasesintropicalclimatecountryusingdeeplearningmodel