应用ARIMA乘积季节模型对耐碳青霉烯类肺炎克雷伯菌流行趋势预测效果研究

doi:10.3969/j.issn.1006-5253.2023.03.003

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Abstract Objective To explore the application value of autoregressive integrated moving average (ARIMA) multiplicative seasonal model in predicting the epidemic trend of carbapenem resistant Klebsiella pneumoniae (CRKP), and to provide a theoretical basis for grasping the epidemic trend and formulating prevention and control measures of CRKP infection in hospitals.Methods Based on the monthly number of CRKP detected in a tertiary hospital in Jiaxing City from 2016 to 2021, the ARIMA multiplicative seasonal model was constructed with SPSS 26.0 software. The actual number of CRKP plants detected in 2022 was selected as the validation data set and compared with the predicted value, so as to evaluate the prediction efficiency of the model.Results The total number of CRKP detected in hospital showed a downward trend year by year from 2016 to 2021. The incidence was concentrated from July to September, and the peak was in September, which was seasonal and periodic. The Bayesian information criterion (BIC) of the fitted ARIMA (0, 1, 2) (0, 1, 0)12 was 0.43, and the mean absolute percent error (MAPE) was 16.54. The difference of Ljung-box test of model residual sequence was not statistically significant (Q=11.06, P=0.81). The residual sequence was white noise, indicating that the model fitted well and was determined as the optimal model. The number of CRKP strains detected from January to December 2022 was used to verify the prediction effect of the model. The results showed that the actual incidence trend was in good agreement with the prediction curve, and the actual values were all within the 95% confidence interval of the predicted values, the prediction error was -33.33%25.00%, and the average relative error was 15.56%, indicating that the prediction effect of the model was good.Conclusion ARIMA multiplicative seasonal model can fit the time change of the number of CRKP detected in the hospital well, so it can be used for short-term prediction and dynamic analysis of CRKP epidemic trend, and provide basis for the early warning and prevention of CRKP infection in hospitals.

Key words： autoregressive integrated moving average model multiplicative seasonal model Carbapenem resistant Klebsiella pneumoniae prediction

Received: 28 February 2023

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	Zhang Qiping
	Ji Conghua
	Lu Jinqi
	Wang Xiaoteng

Cite this article:

Zhang Qiping,Ji Conghua,Lu Jinqi, et al. Application of ARIMA multiplicative seasonal model in predicting the epidemic trend of carbapenem resistant Klebsiella pneumoniae

[J]. journal1, 2023, 30(3): 173-178.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2023.03.003 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2023/V30/I3/173

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