A deep learning based hybrid architecture for weekly dengue incidences forecasting

Zhao, Xinxing; Li, Kainan; Ang, Candice Ke En; Cheong, Kang Hao

doi:10.1016/j.chaos.2023.113170

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…Genomic data have been used to classify COVID-19 variants and other viruses using deep learning approaches [15][16][17]. CNNs and LSTMs have been more frequently used for prediction of dengue cases [3,[18][19][20][21][22][23][24]. That underscores the importance of neural networks combined with genomic sequences as a futuristic method capable of revolutionizing virus studies [25][26][27].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Mumtaz,

Shahbaz,

Qureshi

et al. 2024

Preprint

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Evolution remains an incessant process in viruses, allowing them to elude host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Predicting emerging viral genomes is crucial, particularly in diseases like dengue, where viruses disrupt host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging Dengue Virus (DENV) serotype. While Recurrent Neural Networks (RNN) were originally designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generative model showed accuracy of 93% and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score all to be around 99.00%, demonstrated the classification model’s reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99% similarity to DENV-4 and around 63-65% similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum 90% similarity underscored their uniqueness. We analyzed the conserved motifs in the genome through MEME Suite (version 5.5.5). Our research strives to contribute to the ongoing fight against the Dengue virus by offering predictive insights into its genomic evolution. Looking ahead, proactive predictive modeling before mutations occur holds potential for guiding vaccine design and diagnostic kit development.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Mumtaz,

Shahbaz,

Qureshi

et al. 2024

Preprint

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“…In recent years, Deep Learning has become quite relevant in the field of forecasting (Zhao et al 2023). This section is devoted to describing and present the different deep learning tools used to generate temporal forecasts of Covid-19 in South America, with the LSTM, GRU and Conv1D-LSTM and Conv1D-GRU methods.…”

Section: Deep Learningmentioning

confidence: 99%

“…This new method contains the states of previous times, allowing to store the past time information of the time series, thus capturing the past-future dependence with the influence of current data, but without great success in long-term memory (Aseeri 2023), being specialized in handling sequential data (Zhao et al 2023). This process is done in a hidden layer flow as shown in Figure 1.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

Alfaro,

Vargas,

Fuertes

et al. 2023

Preprint

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The global pandemic of COVID-19 that took place in 2020 and 2021 posed different challenges for health systems worldwide, revealing various deficiencies and generating supply problems and a breakdown in medical services. Given this situation, it is crucial to have predictive methodologies that can accurately estimate the behavior of diseases of this type. This would allow countries to be better prepared in the future and respond effectively to future similar situations, avoiding a repetition of large-scale events. In the literature, deep learning techniques, in particular, have shown promise in this field. In this paper, a comparative study is performed between individual Deep Learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), along with hybrid models that combine Conv1D with LSTM or GRU. The objective is to predict contagion curves in Latin American countries, specifically in Argentina, Brazil, Chile, Colombia, and Peru. These countries present a dwindling number of studies in the existing literature, which makes this work especially relevant. The results emphasize the competitiveness of the hybrid models, which show MAPE values ranging from 0.1–1%. In contrast, the individual models present slightly higher MAPE, in the range of 0.2–0.8%. These results demonstrate the effectiveness of the proposed Deep Learning models in predicting the spread of COVID-19 in South America.

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“…These technologies can be utilized for the surveillance of key patient indicators and the provision of compassionate treatment. In modern sensing technologies, numerous wearable gadgets such as inconspicuous, smart fabrics, and printable electronic tattoos are employed [ 10 , 11 , 12 , 13 ]. The purpose of these gadgets is to collect individual health data in order to anticipate a healthy lifestyle [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction

Kukkar,

Kumar,

Sandhu

et al. 2024

Diagnostics

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Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system’s efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

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A deep learning based hybrid architecture for weekly dengue incidences forecasting

Cited by 11 publications

References 36 publications

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Deep Learning Guided Prediction Modeling of Dengue Virus Evolving Serotype

Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction

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