Abstract：Objective To predict the infection incidence of carbapenemresistant pseudomonas aeruginosa (CRPA) by multiple seasonal autoregressive integrated moving average model（ARIMA）, so as to provide the basis for the development of control strategy of CRPA. Methods The timeseries model of ARIMA was established using CRPA infection data from January 2016 to December 2019 in our hospital, and then the model was validated using the data from January 2020 to September 2020 to evaluate the prediction effect of the model. Results After modeling and fitting, it is concluded that ARIMA（0，1，1）×（0，1，1）12 is the best model. The normalized BIC of model fitting is 3.461, and the coefficient of determination R2 is 0.426. According to the Bayes criterion, the model with the minimum BIC value and the maximum R2 value is the optimal one; the Ljung-Box Q statistics are Q=16.02 and P=0.38, indicating that the residual belongs to the white noise value, and the prediction of this model is relatively suitable. After the model is established, the ARIMA prediction analysis is performed on the CRPA infection rate from January 2020 to September 2020. The results show that the actual incidence trend from January 2020 to September 2020 is relatively consistent with the predicted curve, indicating the accuracy and the forecast of the ARIMA model have a better result. Conclusion ARIMA model can accurately simulate and predict CRPA infection rate and provide a reference for the prevention and control of multi-drug resistant bacteria infection.
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