Multiplicative seasonal model based on ARIMA for prediction of prevalence trend of carbapenem-resistant Acinetobacter baumannii
OBJECTIVE To predict the prevalence trend of carbapenem-resistant Acinetobacter baumannii(CRAB)by using autoregressive integrated moving average(ARIMA)multiplicative seasonal model so as to provide theo-retical basis for formulating targeted prevention and control strategies.METHODS The numbers of the CRAB strains that were monthly isolated from Changzhou Jintan First People's Hospital from 2018 to 2022 were chosen as the dataset.ARIMA multiplicative seasonal model was established,the data of 2023 were set as the validation set and was compared with the predictive value of the model,and the predictive performance of the model was as-sessed.RESULTS The isolation rate of CRAB showed an overall upward trend from 2018 to 2022(P＜0.05).The strains were mainly isolated from sputum specimens,and the prevalence of the strains was at the peak in March-April every year,showing the periodical and seasonal characteristics.The normalized Bayesian information criteri-on(BIC)of the optimal ARIMA(0,1,2)(0,1,1)12 was 3.867,the residual sequence Box-Ljung test showed that there was no significant difference(Q=11.109,P=0.745),and the model was well fitted.The mean relative error of the model was 21.35％in prediction of the number of isolated CRAB strains and the actual values,and the actual values all fell within the 95％confidence interval(95％CI)of the predicted values.CONCLUSION ARI-MA multiplicative seasonal model can predict the prevalence trend of the CRAB strains and provide theoretical bases for short-term prediction,dynamic analysis of the CRAB infection so as to take targeted prevention and control measures.
Autoregressive integrated moving average modelMultiplicative seasonal modelCarbapenem-resist-ant Acinetobacter baumanniiPrevalence trend
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