Biblioteca Virtual en Salud

Objective:

An autoregressive integrated moving average (ARIMA) model was used to predict the number of monthly reported cases of schistosomiasis in China (excluding Hong Kong, Macao and Taiwan), so as to provide a scientific basis for prevention and control of schistosomiasis.

Methods:

Using ARIMA model, taking the time series of monthly reported cases of schistosomiasis in China from January 2009 to December 2018 as the training set, after stabilizing analysis with R 3.6.2 software, ARIMA models were selected by using screening parameters such as akaike information criterion and bayesian information criterion. Taking the number of monthly reported cases of schistosomiasis in China from January to December 2019 as the test set for verification and monthly optimization, an optimal ARIMA model was obtained. The prediction effect of the optimal ARIMA model was verified by the number of monthly reported cases of schistosomiasis in China from January 2019 to October 2020.

Results:

Based on the data of monthly reported cases of schistosomiasis in China from January 2009 to December 2018, four ARIMA models were obtained, namely ARIMA(2,0,2)(1,0,1)[12], ARIMA(2,0,2)(0,0,1)[12], ARIMA(2,0,2)(1,0,0)[12] and ARIMA(2,0,2). By comparing the actual number of cases from January to December 2019 with the predicted values of the four ARIMA models, the optimal prediction model of monthly reported cases of schistosomiasis was ARIMA(2,0,2)(1,0,1)[12], and the mean relative error of the prediction was 0.51%.

Conclusions:

The ARIMA model constructed in this study has high accuracy and is suitable for short-term prediction and analysis of the number of schistosomiasis cases in China. It can provide data support for prevention and control of the disease, and has certain practical guiding significance.