Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordi...
A multimodal deep-learning architecture with epidemic-aware design substantially improves weekly ILI% forecasting in Beijing, particularly at 1–3 week horizons, with meteorological variables gaining prominence at longer lead times.
Accurate multi-week epidemic forecasting requires exploiting the time-lagged predictive structure of heterogeneous surveillance signals, yet most deep learning approaches treat these inputs as temporally synchronized. We present a two-stage framework that first recovers per-feature temporal offsets through a lag-aware,...
: To mitigate and prevent the negative global impacts of epidemics like COVID-19, humanity urgently requires reliable pandemic models to support containment strategies. This paper reviews recent advances in statistical, traditional machine learning, and deep learning models applied to epidemic forecasting and risk asse...
A hybrid deep neural network (HDNN) framework that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, supplemented with dense layers, to enhance predictive accuracy and robustness is proposed.
Air pollution prediction is a key research problem due to its direct impact on public health and the urban environment. Moreover, because air pollutants are dynamic and nonlinear, this poses a complex challenge for time-series modeling. However, while there has been significant development in the use of deep learning–b...
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