IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v97y2024i8d10.1140_epjb_s10051-024-00752-x.html
   My bibliography  Save this article

A deep learning architecture using hybrid and stacks to forecast weekly dengue cases in Laos

Author

Listed:
  • Sathi Patra

    (Indian Institute of Engineering Science and Technology, Shibpur)

  • Soovoojeet Jana

    (Ramsaday College)

  • Sayani Adak

    (Indian Institute of Engineering Science and Technology, Shibpur)

  • T. K. Kar

    (Indian Institute of Engineering Science and Technology, Shibpur)

Abstract

Dengue is an arthropod-borne viral disease prevalent in tropical and subtropical regions. Its adverse impact on human health and the global economy cannot be exaggerated. To improve the efficacy of vector control measures, there is a critical need for mechanisms that can forecast dengue cases with greater accuracy and urgency than before. So, we employ some deep learning techniques using the previous ten years of weekly dengue cases in Laos. A hybrid model combining CNN and stacked LSTM (BiLSTM) is applied along with CNN, LSTM, BiLSTM, and ConvLSTM in this work. Comparing all the outputs we have derived, hybrid CNN and 1 stacked BiLSTM outperform other deep learning models with the one-step-ahead prediction. Further, we have concluded that hybrid CNN and 1 stacked BiLSTM can considerably boost dengue prediction and can be applied in other dengue-prone regions. Graphic abstract

Suggested Citation

  • Sathi Patra & Soovoojeet Jana & Sayani Adak & T. K. Kar, 2024. "A deep learning architecture using hybrid and stacks to forecast weekly dengue cases in Laos," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(8), pages 1-16, August.
    • Handle: RePEc:spr:eurphb:v:97:y:2024:i:8:d:10.1140_epjb_s10051-024-00752-x
    DOI: 10.1140/epjb/s10051-024-00752-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/s10051-024-00752-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1140/epjb/s10051-024-00752-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shah, Kamal & Alqudah, Manar A. & Jarad, Fahd & Abdeljawad, Thabet, 2020. "Semi-analytical study of Pine Wilt Disease model with convex rate under Caputo–Febrizio fractional order derivative," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    2. Jajarmi, Amin & Arshad, Sadia & Baleanu, Dumitru, 2019. "A new fractional modelling and control strategy for the outbreak of dengue fever," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    4. Jiucheng Xu & Keqiang Xu & Zhichao Li & Fengxia Meng & Taotian Tu & Lei Xu & Qiyong Liu, 2020. "Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    5. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    6. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    7. Mokhalad A. Majeed & Helmi Zulhaidi Mohd Shafri & Zed Zulkafli & Aimrun Wayayok, 2023. "A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention," IJERPH, MDPI, vol. 20(5), pages 1-22, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Ahmad, Saeed & Rahman, Mati ur & Arfan, Muhammad, 2021. "On the analysis of semi-analytical solutions of Hepatitis B epidemic model under the Caputo-Fabrizio operator," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    4. Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    5. Vaishnav, Vaibhav & Vajpai, Jayashri, 2020. "Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Villi Dane M. Go, 2023. "Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(2), pages 45-54.
    8. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    9. Abubakar Ahmad Musa & Adamu Hussaini & Weixian Liao & Fan Liang & Wei Yu, 2023. "Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
    10. Ullah, Ihsan & Ahmad, Saeed & Rahman, Mati ur & Arfan, Muhammad, 2021. "Investigation of fractional order tuberculosis (TB) model via Caputo derivative," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    12. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    13. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    14. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    15. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    16. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    18. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    19. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    20. Fatima, Bibi & Zaman, Gul, 2020. "Co-infection of Middle Eastern respiratory syndrome coronavirus and pulmonary tuberculosis," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eurphb:v:97:y:2024:i:8:d:10.1140_epjb_s10051-024-00752-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.