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@#Abstract: Objective To analyze the epidemiological characteristics of pulmonary tuberculosis (PTB) in Ankang City from 2011 to 2021, so as to provide a scientific basis for the formulation of PTB prevention and control strategy. Methods Descriptive statistics were used to analyze the epidemiological characteristics of PTB in Ankang City from 2011 to 2021, and a time series model was established to quantitatively predict the incidence of pulmonary tuberculosis in 2023. Results The incidence rate in Ankang City showed a significant upward trend from 2011 to 2017, and a more obvious downward trend in 2017-2021 (P<0.05), and the decrease rate in 2021 was 40.36% compared with that in 2017. The proportion of etiological positivity increased from 12.5% in 2014 to over 50.00% after 2019. The incidence season was mainly concentrated in the first quarter, accounting for 28.39% of the annual incidence. High incidence areas were concentrated in the south of Ankang: Langao County, Ziyang County and Zhenping County, with 128.32/100 000, 117.07/100 000 and 110.44/100 000, respectively. Low incidence areas were located in the north of Ankang: Ningshan County, with 60.62/100 000. Farmers and students were the high incidence groups, accounting for 81.80% and 4.97% of the total cases respectively. The incidence of young children was relatively low, but cases were reported every year. The incidence rate of male was 2.39 times that of female. The age of onset increased significantly from 15 years old, and the peak incidence was in the age group of 60-<80 years old, followed by the age group of 45-<60 years old, the average annual incidence was 136.44/100 000 and 104.47/100 000, respectively. The model ARIMA(0,1,1)(0,1,1)12 predicted that the incidence of the disease generally increased from October 2022 to March 2023, then steadily decreased, and increased again in December. Conclusions The incidence of tuberculosis varies in different areas of Ankang City, and males, farmers, students and the elderly are all factors of high incidence of tuberculosis. Therefore, different prevention and control strategies should be adopted according to the characteristics of population in different areas. The number of cases in Ankang City in 2023 showed an overall downward trend, which can provide a reference for the prevention and control of PTB.
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ObjectiveTo predict the incidence trend of influenza-like illness proportion （ILI%） in Shanghai using the seasonal autoregressive integrated moving average model （SARIMA）， and to provide an important reference for timely prevention and control measures. MethodsTime series analysis was performed on ILI% surveillance data of Shanghai Municipal Center for Disease Control and Prevention from the 15th week of 2015 to the 52nd week of 2019， and a prediction model was established. Seasonal autoregressive integrated moving average （SARIMA） model was established using data from the foregoing 212 weeks， and prediction effect of the model was evaluated using data from the latter 36 weeks. ResultsFrom the 15th week of 2015 to the 52nd week of 2019， the average ILI% in Shanghai was 1.494%， showing an obvious epidemic peak. SARIMA（1，0，0） （2，0，0） 52 was finally modeled. The residual of the model was white noise sequence， and the true values were all within the 95% confidence interval of the predicted values. ConclusionSARIMA（1，0，0） （2，0，0） 52 can be used for the medium term prediction of ILI% in Shanghai， and can play an early warning role for the epidemic and outbreak of influenza in Shanghai.
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Objective:To analyze the effects of seasonal autoregressive integrated moving average model (SARIMA), generalized additive model (GAM), and long-short term memory model (LSTM) in fitting and predicting the incidence of hemorrhagic fever with renal syndrome (HFRS), so as to provide references for optimizing the HFRS prediction model.Methods:The monthly incidence data of HFRS from 2004 to 2017 of the whole country and the top 9 provinces with the highest incidence of HFRS (Heilongjiang, Shaanxi, Jilin, Liaoning, Shandong, Hebei, Jiangxi, Zhejiang and Hunan) were collected in the Public Health Science Data Center (https://www.phsciencedata.cn/), of which the data from 2004 to 2016 were used as training data, and the data from January to December 2017 were used as test data. The SARIMA, GAM, and LSTM of HFRS incidence in the whole country and 9 provinces were fitted with the training data; the fitted model was used to predict the incidence of HFRS from January to December 2017, and compared with the test data. The mean absolute percentage error ( MAPE) was used to evaluate the model fitting and prediction accuracy. When MAPE < 20%, the model fitting or prediction effect was good, 20%-50% was acceptable, and > 50% was poor. Results:From the perspective of overall fitting and prediction effect, the optimal model for the whole country and Heilongjiang, Shaanxi, Jilin, Liaoning and Jiangxi was SARIMA ( MAPE was 19.68%, 20.48%, 44.25%, 19.59%, 23.82% and 35.29%, respectively), among which the fitting and prediction effects of the whole country and Jilin were good, and the rest were acceptable. The optimal model for Shandong and Zhejiang was GAM ( MAPE was 18.29% and 21.25%, respectively), the fitting and prediction effect of Shandong was good, and Zhejiang was acceptable. The optimal model for Hebei and Hunan was LSTM ( MAPE was 26.52% and 22.69%, respectively), and the fitting and prediction effects were acceptable. From the perspective of fitting effect, GAM had the highest fitting accuracy in the whole country data, with MAPE = 10.44%. From the perspective of prediction effect, LSTM had the highest prediction accuracy in the whole country data, with MAPE = 12.23%. Conclusions:SARIMA, GAM, and LSTM can all be used as the optimal models for fitting the incidence of HFRS, but the optimal models fitted in different regions show great differences. In the future, in the establishment of HFRS prediction models, as many alternative models as possible should be included for screening to ensure higher fitting and prediction accuracy.
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Objective:To study the value of autoregressive integrated moving average (ARIMA) and autoregressive (AR) models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods:Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1, 2021 to December 31, 2021. A time series for the number of ambulances per day was calculated. After identifying the time series prediction model, ARIMA(1,1,1), AR(4) and AR(7) models were obtained. These models were used to predict the number of ambulances per day. ARIMA(1,1,1) model divided the time series into the training set and test set. Prony method was used for parameter calculation, and the demands of number of ambulances of the next few months were forecasted. AR(4) and AR(7) models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results:ARIMA(1,1,1), AR(4) and AR(7) can effectively predict the number of ambulances per day. The prediction fitting error of ARIMA (1,1,1) decreased with the extension of prediction time. The mean absolute percentage error (MAPE) of forecast results of daily vehicle output of emergency dispatching within two months was less than 6% and the predicted results were almost within the 95% confidence interval. The residual analysis of the model verified that the model was significantly effective.Conclusions:ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching, and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.
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Objective@#To analyze the epidemic trend of viral hepatitis in Nanjing from 1989 to 2019 and predict the incidence in 2020, so as to provide reference for the prevention and control of viral hepatitis.@*Methods@#The incidence data of viral hepatitis in Nanjing from 1989 to 2019 was retrieved from Nanjng Center for Disease Control and Prevention and National Infectious Disease Reporting System. The epidemic trend was analyzed by estimating the annual percent change ( APC ) and the average annual percent change ( AAPC ). The seasonal incidence of different types of viral hepatitis was analyzed by seasonal index. The autoregressive integrated moving average model ( ARIMA ) was built to predict monthly incidence rate of viral hepatitis in 2020. @*Results@#The annual incidence rate of viral hepatitis was 62.00/100 000 in Nanjing from 1989 to 2019, showing a downward trend ( AAPC=8.4%, P<0.05 ). From 1998 to 2019, the annual incidence rates of hepatitis A, B, C and E were 1.98/100 000, 14.31/100 000, 2.30/100 000 and 2.60/100 000. The incidence of hepatitis A and B showed downward trends ( AAPC=-11.81%, -6.02%, both P<0.05 ); the incidence trend of hepatitis C was not obvious ( P>0.05 ); the incidence of hepatitis E showed an increasing trend ( AAPC=4.82%, P<0.05 ). From 2015 to 2019, the third and fourth quarters were the epidemic seasons of hepatitis A, B and C, while the first and second quarters were the epidemic seasons of hepatitis E. The ARIMA model predicted that the monthly incidence rates of viral hepatitis in 2020 would range from 1.26/100 000 to 3.69/100 000, among which hepatitis B ranged from 1.21/100 000 to 2.58/100 000, hepatitis C from 0.20/100 000 to 0.48/100 000, hepatitis E from 0.09/100 000 to 0.25/100 000. @*Conclusions@#The incidence of viral hepatitis in Nanjing shows a downward trend. Among different types of hepatitis, hepatitis B has a higher incidence. All types of hepatitis have epidemic seasons. It is predicted that the monthly incidence rates of viral hepatitis will be 1.26/100 000 to 3.69/100 000 in 2020.
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Objective:To use autoregressive integrated moving average （ARIMA） model for predicting the mortality of cardiovascular diseases in residents in Yushui District， Jiangxi Province， and to provide basis for developing the prevention and control strategies as well as to promote the continuous optimization of chronic disease prevention and treatment demonstration area. Methods:Based on the cardiovascular death monitoring data of residents in Yushui District， Jiangxi Province from 2014 to 2018， Econometrics View 9.0 software was used to construct the ARIMA seasonal adjustment model to predict the monthly cardiovascular death in this area. Results:The monthly death rate of cardiovascular diseases in Yushui showed a long-term rising trend， with an apparent seasonal pattern （a peak of cardiovascular death from December to January each year）. After the original sequence was subjected to first-order difference and first-order seasonal difference， the difference sequence showed good stationarity （P<0.05）. All the theoretical models were listed and their model parameters were calculated respectively. After statistical test （P<0.05）， 7 alternative models for seasonal adjustment of ARIMA were selected. Among them， ARIMA（1，1，1）（1，1，1）12 is the optimal model selected in this study （R2=0.749， Adjustment R2=0.724， AIC=8.454， SC=8.633， HQ=8.515）.And its residual sequence was tested by white noise test （P>0.05）， indicating that the prediction effect was good. Conclusion:ARIMA（1，1，1）（1，1，1） 12 model can accurately simulate the long-term trend and seasonal pattern of cardiovascular disease death in Yushui， and make a scientific prediction of the trend and monthly distribution of cardiovascular disease death in the next three years.
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Objective To forecast the trend of mosquito density index in Pudong New Area, Shanghai so as to provide evidence for disease control and risk-control measures for vector-borne diseases. Methods Mosquito monitoring data was collected in Pudong New Area between 2011 and 2015 at the city-level monitoring sites for analysis on the trend of the mosquito density index in Pudong New Area of Shanghai by using the Autoregressive Integrated Moving Average Model (ARIMA). Results From 2011 to 2015, a total of 135 times labor-hour monitoring were carried out at the city-level monitoring points in Pudong New Area.The mosquito density index averaged 6.17/labor-hour with a standard deviation at 4.93, S=[0, 18]/labor-hour.Using ARIMA to analyze the change trend of mosquito density index in Pudong New Area, ARIMA(2, 0, 1)became the final fitting model, with R2=0.808.In the model, the Ljung-Box Q test value was 19.632(AR1=1.866, AR2=-0.907), and MA parameter was 0.999. Conclusion ARIMA model can be used to predict mosquito density monitoring data, but low monitoring frequency and irregular cycle length will affect the prediction results.
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Objective@#Autoregressive integrated moving average (ARIMA) model was used to predict the incidence of tuberculosis in China from 2018 to 2019, providing references for the prevention and control of pulmonary tuberculosis.@*Methods@#The monthly incidence data of tuberculosis in China were collected from January 2005 to December 2017. R 3.4.4 software was used to establish the ARIMA model, based on the monthly incidence data of tuberculosis from January 2005 to June 2017. Both predicted and actual data from July to December 2017 were compared to verify the effectiveness of this model, and the number of tuberculosis cases in 2018-2019 also predicted.@*Results@#From 2005 to 2017, a total of 13 022 675 cases of tuberculosis were reported, the number of pulmonary tuberculosis patients in 2017 was 33.68% lower than that in 2005, and the seasonal character was obvious, with the incidence in winter and spring was higher than that in other seasons. According to the incidence data from 2005 to 2017, we established the model of ARIMA (0,1,2)(0,1,0)12. The relative error between the predicted and actual values of July to December 2017 fitted by the model ranged from 1.67% to 6.80%, and the predicted number of patients in 2018 and 2019 were 789 509 and 760 165 respectively.@*Conclusion@#The ARIMA (0, 1, 2)(0, 1, 0)12 model well predicted the incidence of tuberculosis, thus can be used for short-term prediction and dynamic analysis of tuberculosis in China, with good application value.
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Objective@#To establish a prediction model for infectious disease index（IDI）by autoregressive integrated moving average（ARIMA）,and to provide forcast of infectious diseases to the public. @*Methods@#The data of the percentage of influenza-like illness（ILI）,the incidence rates of hand-foot-mouth disease（HFMD）and other infectious diarrhea（OID）from the 1st week of 2014 to the 14th week of 2018,and Breteau index（BI）from the 1st week of 2016 to the 14th week of 2018 were collected. ARIMA models were built to predict the risk indicators of ILI,HFMD,OID and BI. The weights of the four indicators were evaluated seasonally by the entropy weight method. Then the IDI was calculated and the data of ILI,HFMD, OID and BI from 15th to 19th week in 2018 was used for verification. @*Results@#The forecast was in summer,so IDI=ROUND（0.33×risk index of ILI percentage +0.47×risk index of HFMD incidence +0.10×risk index of OID incidence+0.10×risk index of BI）. The predicted IDI would be 2（less safe）in the whole city and Xiangzhou District,and 1（safe）in Doumen District and Jinwan District. The consistency rates of IDI prediction was 97.50%,95.00%,97.50%,85.00% and 77.50% from 15th to 19th week in 2018,respectively.@*Conclusion@#It was feasible to use IDI for short-term risk prediction of infectious diseases.
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Objective To explore the value of time series analysis and model construction in predicting hand-foot-mouth disease(HFMD). Methods By analyzing the data of HFMD in a hospital in Zhengzhou from January 2009 to October 2016,a seasonal autoregressive moving average(ARMA)model was established according to the time series analysis. At the same time ,the model was evaluated to determine the fitting precision between the measured values and fitted values. Results After the parameter exploration ,the best fitting model was finally determined as ARIMA(1,0,1)(1,0,1)12,with a stationary R2 of 0.734,a statistic value of Ljung-Box Q(18)of 10.497,P Value of 0.725. The two curves of the fitted values and the measured values were close,suggesting that the model has good fitting ability. Conclusion Time series analysis and the seasonal ARMA model have good predictive ability in predicting HFMD.
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Objective To explore the value of the autoregressive integrated moving average model (ARIMA) applied to predict monthly incidence of syphilis so as to provide basis for prevention and control of syphilis . Methods Eviews 8 .0 was used to establish the ARIMA model based on the data of monthly incidence of syphilis in China from January 2009 to December 2015 .Then the data of the first half of 2016 were used to verify the predicted results .The predictions were evaluated by RMSE ,MAE ,MAPE and MRE models .Then the monthly incidence of syphilis in the second half of 2016 was predicted .Results The optimal model for the monthly incidence of syphilis from January 2009 to June 2016 was the model of ARIMA (2 ,1 ,1) × (0 ,1 ,1)12 ,its equation was (1 - B)(1 - B12 ) (1+0 .820 B)(1+0 .566 B2 ) x2t = (1+0 .365 B) (1+0 .897 B12 )εt ,its parameters are as follows :R2 =0 .832 ,RMSE=0 .181 ,MAE=0 .118 ,MAPE=5 .088 .The predicted monthly incidence values (10-5 ) of the second half of 2016 were 3 .124 ,3 .008 ,2 .906 ,2 .691 ,2 .714 ,and 2 .717 .Conclusion ARIMA model has a relatively good prediction precision .Therefore , it can make short-term prediction based on the evolution trend of monthly incidence of syphilis in China .
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OBJECTIVE:To strengthen application management of antibiotics in outpatients,promote rational use of antibiot-ics,and to provide reference for scientific management and decision-making in the hospital. METHODS:The proportion of outpa-tients receiving antibiotics in total outpatients was analyzed statistically during Jan. 2008-Jun. 2016. Utilization rate data of antibiot-ics in outpatients during 2008-2015 were used to establish Autoregressive integrated moving average model(ARIMA),and the data of the first half of 2016 was used to validate established model;the utilization rate trend of antibiotics in outpatients in the second half of 2016 was predicted. SPSS 20.0 statistical software was adopted for statistical analysis. RESULTS:Established ARIMA(2,1, 0)(2,1,0)12 model has higher fitting degree. There was a small difference between measured value and fitted value of utilization rate of antibiotics in outpatients in 2016. Average absolute error was 0.72%,and average relative error was 4.20%,within 95%confidence interval of fitted value. Dynamic trend of model predicted value was basically consistent with measured value. CONCLU-SIONS:ARIMA model simulates utilization rate trend of antibiotics in outpatients well,can be used for short-term prediction and dynamic analysis of utilization rate trend of antibiotics. However,for long-term prediction,various factors should be considered.
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Objective To compare the accuracy of the seasonal time series decomposition method and autoregressive integrated moving average (ARIMA) in the prediction of incidence of tuberculosis(TB) in order to facilitate early-warning.Methods The seasonal decomposition model and ARIMA model were constructed by SPSS20.0 software based on time series of monthly TB incidence between January 2005 and December 2014 in Urumqi,China.The obtained models were used to forecast the monthly incidence in 2015 and compared with the actual incidence respectively.Results Between 2005 and 2014,the incidence of TB was higher during March,April and May in Urumqi.A linear fitting model and a cubic curve fitting model were constructed by the time series seasonal decomposition method.The mean absolute percentage error (MAPE) of each predicted monthly incidence in 2015 was 18.75% and 92.25%,respectively.The predicted values of the linear model were lower than actual values and the predicted values of the cubic curve model were higher than actual values.An ARIMA (2,1,1) (1,1,0)12 fitting model was established by ARIMA method.The MAPE of each predicted monthly incidence in 2015 was 9.46% and there were no significant differences between the predicted and actual values.Conclusion The ARIMA method is better than the seasonal decomposition method for predicting the monthly incidence of TB in Urumqi.
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Objective To explore the application of autoregressive integrated moving average (ARIMA) model, and ARIMA combined nonlinear autoregressive (ARIMA-NAR) model in predicting bacterial dysentery (BE) incidence. Methods Data of BE monthly incidences from Jan. 2004 to Feb. 2015 in Jiangsu Province were used as fitting samples, the 15-month data from Mar. 2015 to May 2016 were used in the prediction phase. ARIMA model and ARIMA-NAR model were established and the effects of two models were compared according to mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE), in which lower values suggested higher prediction accuracy. Results In the fitting phase, the MAE, MSE and MAPE of the ARIMA model were 0. 177 5, 0. 081 4 and 0. 184 7, respectively, while those of the ARIMA-NAR model were 0. 094 1, 0. 029 5 and 0. 104 6, respectively. In the prediction phase, the MAE, MSE and MAPE of the ARIMA model were significantly higher than those of the ARIMA-NAR model. Conclusion ARIMA-NAR combined model is superior to ARIMA model in predicting the time series of BE incidence in Jiangsu Province, suggesting that ARIMA-NAR model can be used to predict the incidence of BD.
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Objective To explore the effect of the autoregressive integrated moving average model?nonlinear auto?regressive neural network(ARIMA?NARNN)model on predicting schistosomiasis infection rates of population. Methods The ARIMA model,NARNN model and ARIMA?NARNN model were established based on monthly schistosomiasis infection rates from Janu?ary 2005 to February 2015 in Jiangsu Province,China. The fitting and prediction performances of the three models were com?pared. Results Compared to the ARIMA model and NARNN model,the mean square error(MSE),mean absolute error (MAE)and mean absolute percentage error(MAPE)of the ARIMA?NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4,respectively. Conclusion The ARIMA?NARNN model could effectively fit and predict schistosomiasis in?fection rates of population,which might have a great application value for the prevention and control of schistosomiasis.
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Objective To compare the performance of ARIMA model and GRNN model for predicting the incidence of tuberculosis. Methods ARIMA model was set up by Eviews 7.0.0.1 and GRNN model was set up by neural network toolbox of Matlab 7.1 based on the monthly tuberculosis incidence data from January 2004 to December 2012 in China. Monthly tuberculosis incidence data in 2013 were subjected to the two models for testing, and the results were compared between the two groups. Results The Theil unequal coefficients (TIC) were 0.034 and 0.059 for ARIMA model and GRNN model, respectively, indicating that ARIMA model was better than GRNN model to fit with the monthly incidence of tuberculosis in 2013. The absolute value of the relative error for ARIMA model was only 57.19% of GRNN model. Conclusion ARIMA prediction model is more suitable for predicting the incidence of tuberculosis in China, and it is suggested a combination of models should be used to predict the incidence of tuberculosis.
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Objective: To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence. Methods: EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot-mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand-foot-mouth disease incidence from September 2013 to February 2014 were served as the examinedsamples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model. Results: Atfer the data sequence was handled by smooth sequence, model identiifcation and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model iftting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83. Conclusion: hTe multiple seasonal ARIMA is a good prediction model, and the iftting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.
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This research aimed to explore the application of autoregressive integrated moving average (ARIMA) model of time series analysis in predicting road traffic injury (RTI) in China and to provide scientific evidence for the prevention and control of RTI.Database was created based on the data collected from monitoring sites in China from 1951 to 2011.The ARIMA model was made.Then it was used to predict RTI in 2012.The ARIMA model of the RTI cases was Yt=eYt-1+0.456▽Yt-1+et (et stands for random error).The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was no statistical significance.The model fitted the data well.True value of RTI cases in 2011 was within 95％ CI of predicted values obtained from present model.The model was used to predict value of RTI cases in 2012,and the predictor (95％CI) was 207 838 (107 579-401 536).The ARIMA model could fit the trend of RTI in China.
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Objective To analyze the Brucellosis incidence and to predict the trends of the disease in Shanxi province and the national in recent years,which could provide the reference for surveillance,prevention and control of the disease.Methods Brucellosis data which was reported monthly during January 2006 and December 2010 in Shanxi province and the data released by Chinese Center for Disease Control and Prevention during January 2005 and December 2010 were collected.Several indexes,such as the annual increasing number,the development rate,growth rate and other indicators were applied to compare Shanxi province with the national Brucellosis epidemic in recent years.What's more,the seasonal autoregressive integrated moving average model (ARIMA) was fitted respectively with the data of Brucellosis incident number reported monthly,so as to predict the prevalence status in the coming two years by verifying the fitting effect.Results Brucellosis prevalence of Shanxi province reached the peak in 2008,and the incidence number was 5397,which was 900 more than 2007.From the onset of decline after 2008,the prevalence decreased by 17.67％ (906/5128) in 2010.However,national incidence of Brucellosis kept increasing before 2009 and the prevalence increased rapidly from 2007 to 2008,and the growth rate reached 39.16％ (8442/21 560).Although the number of Brucellosis fell by 2041 cases in 2010 than in 2009,the rate of decline was only 5.14％(2041/37 734).The fastigium of Brucellosis was from May to July yearly whether Shanxi province or the country.The ARIMA models of Shanxi province and the nation were ARIMA [(1,0,1)(1,1,0)12] and ARIMA[(1,0,1)(0,1,1)12],respectively,according to the incidence numbers reported monthly.The fitting effect of models showed that the predicted values of the two models were both consistent with the actual situation and all predicted values fell within the 95％ confidence limits,which depicted that they both fitted well.The predicted values depict that the incidence of Brucellosis overall trend was basically stable in Shanxi province,while the numbers in the nation would increase in a small extent in 2011 and 2012.The fastigium of Brucellosis was still from May to July yearly.Conclusions Brucellosis control measures are effective in Shanxi province,incidence of Brucellosis declining.The ARIMA model could predict the number of Brucellosis well,which can provide a valuable reference for the predication and evaluation of Brucellosis epidemic in the future.
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The detection of unusual patterns in the occurrence of diseases is an important challenge to health workers interested in early identification of epidemics. The objective of this study was to provide an early signal of infectious disease epidemics by analyzing the disease dynamics. A two-stage monitoring system was applied, which consists of univariate Box-Jenkins model or autoregressive integrated moving average model and subsequent tracking signals from several statistical process-control charts. The analyses were illustrated on January 2000–August 2009 national measles data reported monthly to the Expanded Programme on Immunization (EPI) in Bangladesh. The results of this empirical study revealed that the most adequate model for the occurrences of measles in Bangladesh was the seasonal autoregressive integrated moving average (3, 1, 0) (0, 1, 1)12 model, and the statistical process-control charts detected no measles epidemics during September 2007–August 2009. The two-stage monitoring system performed well to capture the measles dynamics in Bangladesh without detection of an epidemic because of high measles-vaccination coverage.