Propagation model establishing method considering parameter time-varying characteristics and prediction method thereof

Abstract

The invention discloses a propagation model establishing method considering parameter time-varying characteristics. The propagation model establishing method comprises the steps of classifying crowdsof regions involved in a propagation model; constructing an infection rate expression model and a removal rate expression model; and constructing a final propagation model considering the parameter time-varying characteristics. The invention also discloses a prediction method comprising the propagation model establishment method considering the parameter time-varying characteristics. In the propagation model establishing method considering parameter time-varying characteristics and a prediction method, the characteristic that the infection rate and the shift-out rate in the infectious diseasepropagation kinetic model have time-varying characteristics due to fluctuation of various factors in a real environment is considered, a propagation model establishing method considering kinetic modelparameter time-varying characteristics and a corresponding prediction method are provided; the method has stronger reality and interpretability, considers the real-time change of model parameters, and is high in reliability and good in accuracy; meanwhile, the prediction method provided by the invention also improves the reliability and practicability of an epidemic disease development situationprediction result.

Description

technical field [0001] The invention specifically relates to a method for establishing a propagation model and a method for predicting the time-varying parameters. Background technique [0002] The epidemic transmission model is a mathematical model used to predict the development trend of the epidemic. Most of the current epidemic transmission models use the real statistical data recorded in the previous epidemics of infectious diseases to establish dynamic transmission models. For example, the SI model divides the regional population into two categories: Susceptible and Infectious, and the infected population has a constant infection rate to the susceptible population; for the Black Death in London in 1665-1666 and the 1906 In the Mumbai plague in 1999, some scholars further proposed the SIR model, adding the removed population (Recovered). In the model, the infection rate of the susceptible population into the infected population and the removal rate of the infected popu...

AI Technical Summary

Problems solved by technology

[0003] However, the current propagation models are often established based on the ideal state

Therefore, this makes current propagation models less reliable and less accurate

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