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Objective To predict monthly incidents of extended spectrum β-Lactamases (ESBLs)-producing Escherichia coli in Zhejiang Hospital by establishing multiple seasonal autoregressive integrated moving average (ARIMA) model, so as to provide scientific evidence for reducing the incidents of nosocomial infection of ESBLs producing Escherichia coli. Methods Multiple seasonal ARIMA model was established by monthly records of ESBLs producing Escherichia coli from 2010 to 2016 in Zhejiang hospital. Monthly incidents of ESBLs producing Escherichia coli from 2017 to February 2018 were used to verify the predicted result. The predictions were evaluated by models of mean absolute percent error (MAPE) and bayesian information criterion (BIC) . Results The optional model for the monthly incidence from 2010 to 2016 was ARIMA (0, 1, 1) (0, 1, 1)12. The MAPE was 14.76, BIC was 2.01, and the Ljung-Box statistics value Q was 16.79 (P=0.40) . These parameters suggested a good model fitting. The average relative error between the predictive value and the actual value of the monthly incidents ESBLs producing Escherichia coli from 2017 to February 2018 was 14.08%.The actual values were within the 95% confidence interval. Conclusion The multiple seasonal ARIMA model of ARIMA (0, 1, 1) (0, 1, 1 )12 fits and can be used for short-term prediction and dynamic analysis of the incidents of ESBLs producing Escherichia coli in Zhejiang Hospital.
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Objective To predict the incidence of birth defects in Xi'an using the auto-regressive integrated moving average product seasonal model.Methods In Xi'an,the trend of the incidence of birth defects was analyzed and tested from October 2009 to August 2015.Using the data from September to December 2015,the actual birth defects were compared with the model fitting data to evaluate the predictive performance of the model.Multiple seasonal ARIMA model was then fitted under time series to predict the incidence of birth defects in 2016.Results Seasonal effect was seen in the incidence of birth defects in Xi'an.A multiple seasonal ARIMA(0,0,1) (0,1,1)12 was established.The mean of absolute error and the relative error were 9.5 and 0.084,respectively,when compared to the simulated number of patients from September to December in 2015,suggesting that ARIMA (0,0,1) (0,1,1)12 has a better predictive ability.Results under the prediction of multiple seasonal ARIMA model showed that the number of patients in 2016 was similar to that of 2015 in Xi'an,with a slight increase and a decrease in the peak value.Conclusion Multiple seasonal ARIMA(0,0,1)(0,1,1)12 model could be used to successfully predict the incidence of birth defects in Xi'an.
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Objective To examine the feasibility' of using multiple seasonal autoregressive integrated moving average (SARIMA) model for predicting pulmonary tuberculosis (TB) incidence, so as to provide scientific evidence for the prevention and treatment of TB. Methods EViews 7.0.01 software was used to create a SARIMA fit model for seasonal incidence of TB on a monthly basis from January 2004 to December 2012, and the predicting performance of the model was tested with TB data from January to December in 2013. Results The established SARIMA (2,0,2) × (0,1,1)12 model could better fit with the previous TB incidence; and it basically well predicted the TB incidence of the 12 months of 2013, with the mean absolute error being 0. 416 992 and the mean absolute error rate being 5.350 8%. Conclusion The established multiplicative SARIMA model can better simulate and predict the trend of TB incidence with time, and it may have a future in predicting the incidence of TB.
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Objective To apply the 'auto-regressive integrated moving average product seasonal model' in predicting the number of hand,foot and mouth disease in Shaanxi province.Methods In Shaanxi province,the trend of hand,foot and mouth disease was analyzed and tested,under the use of R software,between January 2009 and June 2015.Multiple seasonal ARIMA model was then fitted under time series to predict the number of hand,foot and mouth disease in 2016 and 2017.Results Seasonal effect was seen in hand,foot and mouth disease in Shaanxi province.A multiple seasonal ARIMA (2,1,0) × (1,1,0)12 was established,with the equation as (1-B)(1-B12)(1-1.000B)Ln(Xt) =(1-0.532B-0.363B2) (1-0.644B12-0.454B212)εt.The mean of absolute error and the relative error were 531.535 and 0.114,respectively when compared to the simulated number of patients from Jun to Dec in 2015.Results under the prediction of multiple seasonal ARIMA model showed that the numbers of patients in both 2016 and 2017 were similar to that of 2015 in Shaanxi province.Conclusion Multiple seasonal ARIMA (2,1,0) × (1,1,0)12 model could be used to successfully predict the incidence of hand,foot and mouth disease in Shaanxi province.
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Objective To apply the 'auto-regressive integrated moving average product seasonal model' in predicting the number of hand,foot and mouth disease in Shaanxi province.Methods In Shaanxi province,the trend of hand,foot and mouth disease was analyzed and tested,under the use of R software,between January 2009 and June 2015.Multiple seasonal ARIMA model was then fitted under time series to predict the number of hand,foot and mouth disease in 2016 and 2017.Results Seasonal effect was seen in hand,foot and mouth disease in Shaanxi province.A multiple seasonal ARIMA (2,1,0) × (1,1,0)12 was established,with the equation as (1-B)(1-B12)(1-1.000B)Ln(Xt) =(1-0.532B-0.363B2) (1-0.644B12-0.454B212)εt.The mean of absolute error and the relative error were 531.535 and 0.114,respectively when compared to the simulated number of patients from Jun to Dec in 2015.Results under the prediction of multiple seasonal ARIMA model showed that the numbers of patients in both 2016 and 2017 were similar to that of 2015 in Shaanxi province.Conclusion Multiple seasonal ARIMA (2,1,0) × (1,1,0)12 model could be used to successfully predict the incidence of hand,foot and mouth disease in Shaanxi province.