https://doi.org/10.1140/epjb/s10051-024-00752-x
Regular Article - Statistical and Nonlinear Physics
A deep learning architecture using hybrid and stacks to forecast weekly dengue cases in Laos
1
Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, 711103, Howrah, West Bengal, India
2
Department of Mathematics, Ramsaday College, Amta, 711401, Howrah, West Bengal, India
Received:
28
February
2024
Accepted:
16
July
2024
Published online:
1
August
2024
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.
Soovoojeet Jana, Sayani Adak and T. K. Kar have contributed equally to this work.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
