This study integrates Temporal Convolutional Networks (TCNs) and a Multi-Modal Adaptive Fusion (MAF) mechanism to predict three levels of dengue severity: mild, moderate and severe, offering a clinically meaningful and interpretable decision-support tool for early dengue severity prediction, facilitating timely intervention and informed medical decision-making.
A comprehensive review of CI models for outbreak prediction, comparing supervised and unsupervised methods such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and hybrid models.
Z. Abdullahi· International Journal of App...· 0 citations
The feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan, Puerto Rico and Iquitos, Peru is examined, demonstrating that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases.
Pratik S. Machchar, Purvi N. Ramanuj, R. Patel et al.· International Journal of Inf...· 0 citations
Findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations, than LSTM.
George Elmar, Asriyanik, Winda Apriandari· Kontribusia (Research Dissem...· 0 citations
Dengue is an arboviral disease of high public health relevance, characterized by pronounced temporal variability, nonlinearity, and recurrent outbreaks, which pose challenges to epidemiological surveillance and decision-making. This study evaluated the performance of machine learning methods for short-term forecasting...
D. C. da Cunha e Silva, L. M. Nery, Nícholas de Paula Nicomedes et al.· International journal of bio...· 0 citations
Dengue fever poses a major global health threat, with Brazil experiencing severe recurrent outbreaks driven by climatic, socio-economic and mobility factors. Accurate prediction remains challenging owing to dynamically shifting transmission patterns under intervention. This study employs a physics-informed neural netwo...
Dan-Yang Li, Wei-De Li, Hao-Tian Zhang et al.· Journal of the Royal Society...· 0 citations
A spatio-temporal deep learning framework based on the U-Net++ architecture is proposed to generate high-resolution dengue risk maps in Colombia and highlights the potential of integrating heterogeneous climatic, environmental, and socioeconomic data within a spatiotemporal deep learning framework to characterize dengu...
Daira Velandia, J. Contador, Juan Zamora et al.· Scientific Reports· 0 citations
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