The application provides an influenza prediction method,
system, device and medium based on epidemic dynamics, and relates to the technical field of epidemic transmission prediction, which realizes collaborative modeling of data-driven and mechanism-driven by fusing multiple source
monitoring data and epidemic transmission mechanism; in the
feature engineering stage, the influenza
monitoring data is structured and features are extracted, forming influenza transmission features with physical meaning; in the prediction stage, a
physical information neural
network model based on a deep neural network framework is used, which includes an input layer, a multi-layer feedforward network, a time embedding representation layer and an output layer, and is trained by fusing an
observation data fitting and a composite
loss function constrained by the epidemic dynamics mechanism, so that the model can fit the historical
monitoring data and strictly follow the internal dynamics law of
disease transmission, thereby significantly improving the accuracy,
interpretability, extrapolation stability and robustness to small samples and
noise interference of the prediction result.