Research On The Application Of Infectious Disease Prediction Based On Time Series

Infectious diseases can do great harm to people’s life.Light can cause discomfort in the patient’s body,and the weight can seriously affect the dysfunction of various organs and even life.Therefore,it is very important and urgent to predict the incidence of infectious diseases more accurately.The prediction of the incidence of infectious diseases can not only provide references for relevant departments,make correct decisions in advance,but also enhance people’s awareness of the risk of infectious diseases,so that infectious diseases can be prevented and controlled to some extent.This paper uses the method of time series prediction to analyze and study the incidence of infectious diseases of class B in Yunnan province.The incidence rate is forecasted by BP neural network and ARIMA model.Finally,a new combination model is formed by combining BP neural network model with ARIMA model.The main work of this paper is as follows:First of all,the grey prediction model was established based on annual population data of Yunnan province.The annual population data of Yunnan province in 2017 and 2018 were predicted with 2005-2016 year data.The results show that the prediction level of the model is outstanding,which shows that the model can be used well in the population data prediction of Yunnan province.Secondly,the incidence of infectious diseases of class B in Yunnan province is very cyclical and seasonal,and the ARIMA model can describe these characteristics well.The ARIMA model is established for the incidence of infectious diseases data.The prediction result is very good by using the model Using this model.The ARIMA model can be effectively applied to the prediction of the incidence of infectious diseases of class B in Yunnan province.Thirdly,in view of the non-linear characteristics of the incidence of infectious diseases of Class B in Yunnan Province,the BP neural network model is established.The BP neural network model has good non-linearity and adaptability,the results show that BP neural network has good predictive ability,The BP neural can be effectively applied to the prediction of the incidence of infectious diseases of class B in Yunnan province.Finally,the residual ARIMA model is white noise sequence,has highly nonlinear characteristics.In order to further improve the accuracy and prediction effect,we combine ARIMA model and BP neural network model to form a new combination model— ARIMA_BP combination model.The several models were evaluated by MAPE,RMSE and MAE.The results showed that the ARIMA_BP combination model was better than the ARIMA model and the BP neural network model in the prediction of class B infectious diseases in Yunnan province.