Résumé
Objective To compare the effect of ARIMA models with and without covariates in predicting the number of HIV infections among young students in Dalian. Methods First, univariate correlation analysis was performed on the network, STD sequence and HIV sequence to understand whether there was a correlation and lag relationship between them. Secondly, variables with the strongest correlation and predictive value and HIV infection numbers were used as the baseline data to establish an ARIMA model with covariates and a general ARIMA model without covariates, and to predict the HIV number from 2019 to 2021. The average absolute errors were used as evaluation indexes to compare the prediction effects of the two models. Results A total of 841 cases of HIV infection among young students were reported in Dalian from 2013 to 2021. The results of univariate correlation analysis showed that the search index of the keyword AIDS in the Baidu Index in a given month from 2013 to 2019 was significantly positively correlated with the number of HIV infections in that month (r=0.302, P=0.006), and gonorrhea was negatively correlated with the number of HIV infections with a lag of 2 months (r=-0.250, P =0.024). Using gonorrhea incidence number and HIV infection number as the basic data, an ARIMA model with covariates and a general ARIMA model without covariates were established to predict the number of HIV infection among young students from 2019 to 2021, and the average absolute errors were 17.621% and 66.17%, respectively. Conclusion Compared with the general ARIMA model without covariates, the ARIMA model based on the combined use of STD incidence and HIV infection is more suitable for predicting the number of HIV infections among young students in Dalian, but the average absolute error of the model is still large, which needs further improvement in the future research.