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| Prediction of infection incidence of carbapenem-resistant pseudomonas aeruginosa by ARIMA time series model |
| Gao Shengchun, Wu Hongmei |
| Nosocomial Infection Control Department, Wenzhou People′s Hospital, Wenzhou 325000, China |
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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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Received: 26 February 2021
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［1］HU Y Y, CAO J M, YANG Q, et al. Risk factors for carbapenem-resistant pseudomonas aeruginosa, Zhejiang Province, China［J］. Emerg Infect Dis, 2019,25(10):1861-1867.
［2］YANG P, CHEN Y, JIANG S, et al. Association between antibiotic consumption and the rate of carbapenem-resistant Gram-negative bacteria from China based on 153 tertiary hospitals data in 2014［J］. Antimicrob Resist Infect Control, 2018,7:137.
［3］李小菊．耐碳青霉烯类铜绿假单胞菌感染危险因素、耐药分析及分子流行病学研究［D］．南昌：南昌大学，2019．
［4］VERANO D A, HusNAWATI H, ERMATITA E. Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry［J］. Journal of Information Technology and Computer ence, 2020,5(1):13．
［5］ORTEGA J A, EUGENIO L, ROBERTO B, et al. Validation of an AutoRegressive Integrated Moving Average model for the prediction of animal zone temperature in a weaned piglet building［J］. Biosystems Engineering, 2018,174:231238．
［6］李娇，商临萍，郭红菊，等．综合ICU多重耐药菌医院感染的风险模型构建［J］．中国感染控制杂志，2016，15(10)：730-734．
［7］刘梦元，方芳，杨富，等．肝移植术后多重耐药菌感染风险预测模型的验证［J］．上海护理，2019，19(7)：10-13．
［8］WEBB B J, DASCOMB K, STENEHJEM E, et al. Derivation and multicenter validation of the drug resistance in pneumonia clinical prediction score［J］. Antimicrob Agents Chemother, 2016,60(5):2652-2663.
［9］周水森，黄芳，沈毓祖．ARIMA模型在疟疾发病预测中的应用［J］．中国病原生物学杂志，2007，2(4)：284-286．
［10］陶长余，章士军，陈郁．南通市水痘疫情时间序列分析［J］．中国学校卫生，2015，36(8)：1220-1222．
［11］储文杰，金凯玲，林凯，等．基于ARIMA乘积季节模型预测产超广谱β-内酰胺酶大肠埃希菌流行趋势研究［J］．预防医学，2018，30(7)：680-684．
［12］刘燕，薛凌波，于晓强，等．多重耐药鲍曼不动杆菌医院感染发展趋势分析［J］．中国消毒学杂志，2018，35(5)：345-348．
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