Abstract:

An autoregressive integrated moving average model (ARIMA) based on the characteristics of infectious diseases changes is proposed for accurate prediction of the epidemics.Firstly,the original data is preprocessed smoothly by the model,which is also used to eliminate the abruptness and the characteristics of seasonal and cyclical.Then the ARIMA is used to build a model of data that has been smoothed.We use the data,the morbidity of influenza in a city,to simulate the results of the experiment,which shows that ARIMA can capture the regulation of the variation of infectious diseases well,and improve the accuracy of the prediction.It is an efficient approach to predicting the epidemic,which also provides support for decision-making in epidemic prediction and monitoring.

Key words: infectious disease;stationary time series;pretreatment;ARIMA