Infectious disease trend prediction method based on neural network and SEIR model

Abstract

The invention relates to the field of artificial intelligence prediction, in particular to an infectious disease trend prediction method based on a neural network and an SEIR model, and the method comprises the following steps: 1, obtaining a data set; 2, performing data preprocessing; 3, constructing an epidemic situation trend prediction model composed of a virus infection rate prediction module and an epidemic situation trend prediction module; 4, training an epidemic situation trend prediction model by using the data preprocessed in the step 2, and setting a loss function and a model parameter updating mode of the epidemic situation trend prediction model; 5, predicting the epidemic situation trend by using the epidemic situation trend prediction model trained in the steps 1-4. According to the method, less training data can be used, effective, automatic, dynamic and real-time prediction can be carried out on the epidemic situation trend of infectious diseases, and human intervention is not needed in the prediction process.

Description

technical field [0001] The invention relates to the field of artificial intelligence forecasting, in particular to a method for predicting infectious disease trends based on neural networks and SEIR models. Background technique [0002] For the prediction of infectious disease epidemics, with the development of machine learning in recent years, prediction methods can be divided into traditional methods that do not use machine learning methods and methods that use machine learning. Traditional methods use traditional static mathematical models of infectious diseases (such as: SIR model, SEIR model, etc.) to model and predict the spread of the epidemic. [0003] In the traditional method of forecasting the epidemic trend of infectious diseases, the parameters in the forecasting model cannot be adaptive, so that dynamic forecasting cannot be realized, and the model parameters are set by experience, and the influence of manual intervention is relatively large; in the method usin...

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Problems solved by technology

[0003] In the traditional method of forecasting the epidemic trend of infectious diseases, the parameters in the forecasting model cannot be adaptive, so that dynamic forecasting cannot be realized, and the model parameters are set by experience, and the influence of manual intervention is relatively large; in the method using machine learning, or It is difficult to make a comprehensive prediction of the epidemic trend because the timing information in the data is not considered, or the various groups of people in the epidemic are not fully grasped

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