https://doi.org/10.4081/gh.2023.1176
Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach
Submitted: 28 November 2022
Accepted: 19 April 2023
Published: 25 May 2023
Accepted: 19 April 2023
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Supplementary Materials: 52
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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This research proposes a ‘temporal attention’ addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.
Altassan KK. (2020). Environmental and population factors influencing dengue fever emergence and spread in Saudi Arabia (Doctoral dissertation).
Anggraeni W, Sumpeno S, Yuniarno EM, Rachmadi RF, Gumelar AB, Purnomo MH, 2020. Prediction of dengue fever outbreak based on climate factors using fuzzy-logistic regression. In 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 199-204.
DOI: https://doi.org/10.1109/ISITIA49792.2020.9163708
Anno S, Hara T, Kai H, Lee MA, Chang Y, Oyoshi K, Tadono T, 2019. Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning. Geospatial Health 14:771.
DOI: https://doi.org/10.4081/gh.2019.771
Appice A Gel YR, Iliev I, Lyubchich V & Malerba D. (2020). A multi-stage machine learning approach toto predict dengue incidence: a case study in Mexico. IEEE Access 8:52713-52725.
DOI: https://doi.org/10.1109/ACCESS.2020.2980634
Baquero OS, Santana LMR, Chiaravalloti-Neto F, 2018. Dengue forecasting in São Paulo City with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PloS One 13:e0195065.
DOI: https://doi.org/10.1371/journal.pone.0195065
Bogado JV, Stalder D, Gómez S, Schaerer C, 2020. Deep learning-based dengue cases forecasting with synthetic data. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 7(1).
Cousien A, Ledien J, Souv K, Leang R, Huy R, Fontenille D, Cauchemez S, 2019. Predicting dengue outbreaks in Cambodia. Emerging Infect Dis 25:2281.
DOI: https://doi.org/10.3201/eid2512.181193
Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z, 2020. Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403:348-359.
DOI: https://doi.org/10.1016/j.neucom.2020.04.110
Diong JY, Yip WS, MatAdam MK, Chang NK, Yunus F, Abdullah MH, 2015. The definitions of the southwest monsoon climatological onset and withdrawal over Malaysian region. Malaysian Meteorology Dep 3:1-30.
Donahue J, Anne HL, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T, 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference Onon Computer Vision Andand Pattern Recognition (pp. 2625-2634).
DOI: https://doi.org/10.1109/CVPR.2015.7298878
Fathima S, Hundewale N, 2011. Comparison of classification techniques-SVM and naives bayes to predict the arboviral disease-dengue. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 538-539.
DOI: https://doi.org/10.1109/BIBMW.2011.6112426
Ferdousi T, Cohnstaedt LW, Scoglio CM, 2021. A windowed correlation based feature selection method toto improve time series prediction of dengue fever cases. arXiv preprint arXiv:2104.10289.
DOI: https://doi.org/10.1109/ACCESS.2021.3120309
Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA, 2001. Climate variability and change in the United States: potential impacts on vector-and rodent-borne diseases. Environ Health Perspect 109;223-33.
DOI: https://doi.org/10.1289/ehp.109-1240669
Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neural Computation 9:1735-1780.
DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Idris MFIM, Abdullah A, Fauzi SSM, 2018. Prediction of dengue outbreak in Selangor using fuzzy logic. In Proceedings of the Second International Conference on the Future of ASEAN (ICoFA) 2017–Volume 2: Science and Technology (pp. 593-603). Springer Singapore.
DOI: https://doi.org/10.1007/978-981-10-8471-3_59
Jain R, Sontisirikit S, Iamsirithaworn S, Prendinger H, 2019. Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data. BMC Infect Dis 19:272.
DOI: https://doi.org/10.1186/s12879-019-3874-x
Jayaraj VJ, Avoi R, Gopalakrishnan N, Raja DB, Umasa Y, 2019. Developing a dengue prediction model based on climate in Tawau, Malaysia. Acta Tropica 197:105055.
DOI: https://doi.org/10.1016/j.actatropica.2019.105055
Kolivras KN, 2010. Changes in dengue risk potential in Hawaii, USA, due to climate variability and change. Climate Res 42:1-11.
DOI: https://doi.org/10.3354/cr00861
Kramer O, 2016. Scikit-learn. pp 45-53. In Machine learning for evolution strategies. Studies in Big Data, Vol. 20; Springer.
DOI: https://doi.org/10.1007/978-3-319-33383-0_5
Laptev N, Yosinski J, Li LE, Smyl S, 2017. Time-series extreme event forecasting with neural networks at uber. In International Conference On Machine Learning 34:1-5.
Li Y, Zhu Z, Kong D, Han H, Zhao Y, 2019. EA-LSTM: Evolutionary attention-based LSTM for time series prediction. Knowledge-Based Systems 181:104785.
DOI: https://doi.org/10.1016/j.knosys.2019.05.028
Lim B, Zohren S, 2021. Time-series forecasting with deep learning: a survey. Philosoph Trans Royal Soc A 379:20200209.
DOI: https://doi.org/10.1098/rsta.2020.0209
McMichael AJ, Woodruff RE, Hales S, 2006. Climate change and human health: present and future risks. Lancet 367:859-69.
DOI: https://doi.org/10.1016/S0140-6736(06)68079-3
Men L, Ilk N, Tang X, Liu Y, 2021. Multi-disease prediction using LSTM recurrent neural networks. Expert Systems Appl 177:114905.
DOI: https://doi.org/10.1016/j.eswa.2021.114905
Methiyothin T, Ahn I, 2022. Forecasting dengue fever in France and Thailand using XGBoost. In 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC); pp. 677-680.
DOI: https://doi.org/10.23919/APSIPAASC55919.2022.9979936
Malaysia Ministry of Health Technology Assessment Section. Clinical practice guidelines, 2015.
Mohd-Zaki AH, Brett J, Ismail E, L'Azou M, 2014. Epidemiology of dengue disease in Malaysia (2000–2012): A systematic literature review. PLoS Neglected Trop Dis 8:e3159.
DOI: https://doi.org/10.1371/journal.pntd.0003159
Moten S, Yunus F, Ariffin M, Burham N, Yik DJ, Adam MKM, Sang YW, 2014. Statistics of northeast monsoon onset, withdrawal and cold surges in Malaysia. Guidelines No. 1.
Mussumeci E, Coelho FC, 2020. Machine-learning forecasting for dengue epidemics-comparing LSTM, random forest and lasso regression. medRxiv.
DOI: https://doi.org/10.1101/2020.01.23.20018556
Nan J, Liao X, Chen J, Chen X, Chen J, Dong G, Hu G, 2018. Using climate factors to predict the outbreak of dengue fever. In 2018 7th International Conference on Digital Home (ICDH) (pp. 213-218).
DOI: https://doi.org/10.1109/ICDH.2018.00045
Nayak MSDP, Narayan KA, 2019. Forecasting dengue fever incidence using ARIMA analysis. Int J Collab Res Internal Medicine Public Health 11;924-932.
Polwiang S, 2020. The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017). BMC Infect Dis 20:1-10.
DOI: https://doi.org/10.1186/s12879-020-4902-6
Promprou S, Jaroensutasinee M, Jaroensutasinee K, 2006. Forecasting dengue haemorrhagic fever cases in southern Thailand using ARIMA models. https://apps.who.int/iris/handle/10665/170355
Raffel C, Ellis DP, 2015. Feed-forward networks with attention can solve some long-term memory problems. arXiv preprint arXiv:1512.08756.
Shepard DS, Undurraga EA, Halasa YA, 2013. Economic and disease burden of dengue in southeast Asia. PloS Neglected Trop Dis 7:e2055.
DOI: https://doi.org/10.1371/journal.pntd.0002055
Siregar FA, Makmur T, 2019. Time series analysis of dengue hemorrhagic fever cases and climate: a model for dengue prediction. J Physics 1235:012072.
DOI: https://doi.org/10.1088/1742-6596/1235/1/012072
Souza C, Maia P, Stolerman LM, Rolla V, Velho L, 2022. Predicting dengue outbreaks in Brazil with manifold learning on climate data. Expert Systems Applications 192:116324.
DOI: https://doi.org/10.1016/j.eswa.2021.116324
Suppiah J, Ching S, Amin-Nordin S, Low G, 2018. Clinical manifestations of dengue in relation to dengue serotype and genotype in Malaysia: A retrospective observational study. PLoS Neglected Trop Dis 12:17.
DOI: https://doi.org/10.1371/journal.pntd.0006817
WHO, 2023, Dengue and severe dengue, fact sheet of 17 March 2023 Accessed on 26 March 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
Wong CL, Venneker R, Uhlenbrook S, Jamil ABM, Zhou Y, 2009. Variability of rainfall in peninsular Malaysia. Hydrology Earth System Sci Disc 6:5471-5503.
DOI: https://doi.org/10.5194/hessd-6-5471-2009
Xu J, Xu K, Li Z, Meng F, Tu T, Xu L, Liu Q, 2020. Forecast of dengue cases in 20 Chinese cities based on the deep learning method. Int J Environ Res Public Health 17:453.
DOI: https://doi.org/10.3390/ijerph17020453
Xu J, Xu K, Li Z, Tu T, Xu L, Liu Q, 2019. Developing a dengue forecast model using long short term memory neural networks method. bioRxiv, 760702.
DOI: https://doi.org/10.1101/760702
Yuan HY, Wen TH, Kung YH, Tsou HH, Chen CH, Chen LW, Lin PS, 2019. Prediction of annual dengue incidence by hydro-climatic extremes for southern Taiwan. Int J Biometeorol 63:259-268.
DOI: https://doi.org/10.1007/s00484-018-01659-w
Zhao N, Charland K, Carabali M, Nsoesie EO, Maheu-Giroux M, Rees E, Zinszer K, 2020. Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLOS Neglected Trop Dis 14:e0008056.
DOI: https://doi.org/10.1371/journal.pntd.0008056
Zheng H, Lin F, Feng X, Chen Y, 2020. A hybrid deep learning model with attention-based conv-lstm networks forfor short-term traffic flow prediction. IEEE Trans Intelligent Transport Syst 22;6910-6920.
DOI: https://doi.org/10.1109/TITS.2020.2997352
Zhu G, Xiao J, Zhang B, Liu T, Lin H, Li X, Hao Y, 2018. The spatiotemporal transmission of dengue and its driving mechanism: a case study on the 2014 dengue outbreak in Guangdong, China. Sci Total Environment 622:252-259.
DOI: https://doi.org/10.1016/j.scitotenv.2017.11.314
Zhu L, Laptev N, 2017. Deep and confident prediction for time series at uber. In 2017 IEEE International Conference on Data Mining Workshops, pp. 103-110.
DOI: https://doi.org/10.1109/ICDMW.2017.19
How to Cite
Majeed, M. A. ., Shafri, H. Z. M., Wayayok, A., & Zulkafli, Z. (2023). Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1176
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