重庆市HIV感染和艾滋病新发报告总数预测模型的拟合和比较分析
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摘要
目的探讨适合于重庆市人类免疫缺陷病毒(human immunodeficiency virus,HIV)感染和艾滋病疫情特点的预测方法,并对近2年的发展变化进行短期预测并验证其结果。方法采用ARIMA模型、指数平滑法和趋势外推法3种常见的时间序列模型对2006-2015年重庆市新报告的HIV/AIDS病例总数进行拟合,对2016和2017年的新报告HIV/AIDS病例数进行预测并验证,构建并比较适合于重庆市艾滋病疫情的模型和预测方法。结果根据模型识别和诊断结果,分别构建了3种时间序列模型用于拟合和预测重庆市HIV/AIDS新发报告总数,分别是ARIMA(0,1,1)(0,1,1)模型、简单季节性指数平滑模型和二次曲线模型。3种模型对流行趋势拟合的标准误差为62. 34、45. 51和227. 29,对近2年新发总数预测的平均相对误差分别为4. 51%、2. 14%和12. 74%。结论 3种预测模型对重庆市HIV/AIDS流行趋势的拟合效果和预测结果不同,简单季节性指数平滑法能更准确地拟合和预测重庆市HIV/AIDS新发总人数。
Objective To establish prediction models for the total number of newly reported cases of HIV infection and AIDS in Chongqing and assess the performance of these models using the data in the recent2 years. Methods The ARIMA model,exponential smoothing method and trend extrapolation method were used to fit the data of the total number of newly reported cases of HIV infection and AIDS in Chongqing collected from 2006 to 2015. The number of newly reported cases in 2016 and 2017 were used to verify the performance of the 3 prediction models to establish the optimal model for epidemic prediction of AIDS in Chongqing. Results Based on the results of model identification and diagnosis,3 time series models,namely ARIMA( 0,1,1)( 0,1,1) model,simple seasonal exponential smoothing model and quadratic curve model,were constructed to fit and predict the total number of new HIV/AIDS cases in Chongqing. The standard errors of the 3 models for fitting the epidemic trends were 62. 30,45. 51 and 227. 29,respectively,and their average relative errors for predicting the total number of new cases in the next two years were4. 51%,2. 14% and 12. 74%,respectively. Conclusion The 3 prediction models have different fitting effects and performance in predicting HIV/AIDS epidemic in Chongqing. Among them,the simple seasonal exponential smoothing method can more accurately fit and predict the total number of new HIV/AIDS cases in Chongqing.
Objective To establish prediction models for the total number of newly reported cases of HIV infection and AIDS in Chongqing and assess the performance of these models using the data in the recent2 years. Methods The ARIMA model,exponential smoothing method and trend extrapolation method were used to fit the data of the total number of newly reported cases of HIV infection and AIDS in Chongqing collected from 2006 to 2015. The number of newly reported cases in 2016 and 2017 were used to verify the performance of the 3 prediction models to establish the optimal model for epidemic prediction of AIDS in Chongqing. Results Based on the results of model identification and diagnosis,3 time series models,namely ARIMA( 0,1,1)( 0,1,1) model,simple seasonal exponential smoothing model and quadratic curve model,were constructed to fit and predict the total number of new HIV/AIDS cases in Chongqing. The standard errors of the 3 models for fitting the epidemic trends were 62. 30,45. 51 and 227. 29,respectively,and their average relative errors for predicting the total number of new cases in the next two years were4. 51%,2. 14% and 12. 74%,respectively. Conclusion The 3 prediction models have different fitting effects and performance in predicting HIV/AIDS epidemic in Chongqing. Among them,the simple seasonal exponential smoothing method can more accurately fit and predict the total number of new HIV/AIDS cases in Chongqing.
引文
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[12]马玉秀. 2006-2015年济南市槐荫区法定传染病流行趋势及预测分析[D].济南:山东大学,2016.MA Y X. The analysis and predicting of trend of notifiable infectious diseases in Huaiyin from 2006-2015[D]. Jinan:Shangdong University,2016.
[13]龚磊,吴家兵,侯赛,等.曲线估计在流行性感冒发病预测中的应用[J].华南预防医学,2017,43(1):30-33. DOI:10. 13217/j. scjpm. 2017. 0030.GONG L,WU J B,HOU S,et al. Application of curvilinear estimation in predicting incidence of influenza[J]. South China J Prev Med,2017,43(1):30-33. DOI:10.13217/j. scjpm. 2017. 0030.
[14]朱宗元,于青. ARIMA模型在我国病毒性肝炎发病率预测中的应用[J].中国卫生统计,2011,28(1):65-67.DOI:10. 3969/j. issn. 1002-3674. 2011. 01. 022.ZHU Z Y,YU Q. Application of ARIMA model in predicting the incidence of viral hepatitis in China[J]. Chin J Health Stat,2011,28(1):65-67. DOI:10. 3969/j. issn.1002-3674. 2011. 01. 022.
[15]赛晓勇,张治英,徐德忠,等.不同时间序列分析法在洞庭湖区血吸虫病发病预测中的比较[J].中华流行病学杂志,2004,25(10):863-866. DOI:10. 3760/j.issn:0254-6450. 2004. 10. 010.SAI X Y,ZHANG Z Y,XU D Z,et al. Application of“time series analysis”in the prediction of schistosomiasis prevalence in areas of “breaking dikes or opening sluice for waterstore”in Dongting Lake areas,China[J]. Chin J Epidemiol,2004,25(10):863-866. DOI:10. 3760/j. issn:0254-6450. 2004. 10. 010.
[16]董蒴,廖小勇,任全玉.艾滋病预测模型及其应用研究-基于灰色系统理论[J].现代商贸工业,2011(8):25-26. DOI:10. 3969/j. issn. 1672-3198. 2011. 08. 015.DONG S,LIAO X Y,REN Q Y. Research on AIDS prediction model and its prediction based on grey system theory[J]. Mod Bus Trade Ind,2011(8):25-26. DOI:10.3969/j. issn. 1672-3198. 2011. 08. 015.
[2]博克斯,詹金斯,莱因泽尔.时间序列分析:预测与控制[M].王成璋,尤梅芳,郝杨,译.北京:机械工业出版社,2011:51.BOX G E P,JENKINS G M,REINSEL G C. Time series analysis:forecasting and control[M]. WANG C Z,YOU M F,HAO Y,Translated. Beijing:China Machine Press,2011:51.
[3]范引光,吕金伟,戴色莺,等. ARIMA模型与灰色预测模型GM(1,1)在HIV感染人数预测中的应用[J].中华疾病控制杂志,2012,16(12):1100-1103.FAN Y G,LYU J W,DAI S Y,et al. Prediction on the number of HIV with models of ARIMA and GM(1,1)[J].Chin J Dis Control Prev,2012,16(12):1100-1103.
[4]杨永利,毛赛彩,薛源,等. GM(1,1)和趋势外推模型在我国艾滋病发病率预测中的应用[J].中国卫生统计,2014,31(6):952-954.YANG Y L,MAO S C,XUE Y,et al. Prediction on the incidence rate of AIDS in China with gray model(1,1)and trend extrapolation model[J]. Chin J Health Stat,2014,31(6):952-954.
[5]薛薇. SPSS统计分析方法及应用[M]. 3版.北京:电子工业出版社,2013.XUE W. SPSS statistical analysis method and application[M].3 Ed. Beijing:Electronic Industry Press,2013.
[6]吴家兵,叶临湘,尤尔科.时间序列模型在传染病发病率预测中的应用[J].中国卫生统计,2006,23(3):276.WU J B,YE L X,YOU E K. Application of time series model in prediction of incidence rate of infectious diseases[J].Chin J Health Stat,2006,23(3):276.
[7]丁亚兴,张之伦,朱向军.自回归综合移动平均模型对天津市甲型肝炎发病预测[J].疾病监测,2008,23(5):326-328. DOI:10. 3784/j. issn. 1003-9961. 2008. 05. 024.DING Y X,ZHANG Z L,ZHU X J. Prediction of the incidence of hepatitis A in Tianjin using the autoregressive integrated moving average model[J]. Dis Surveillance,2008,23(5):326-328. DOI:10. 3784/j. issn. 1003-9961. 2008.05. 024.
[8]王昕,程小雯,房师松,等.指数平滑模型在流感样病例预测中的应用[J].中国热带医学,2011,11(8):938-939. DOI:10. 13604/j. cnki. 46-1064/r. 2011. 08. 023.WANG X,CHENG X W,FANG S S,et al. Application of exponential smoothing model in forecasting influenza incidence[J]. China Tropical Med,2011,11(8):938-939. DOI:10. 13604/j. cnki. 46-1064/r. 2011. 08. 023.
[9]谢妮.深圳市HIV/AIDS疫情分布特征与预测模型比较[D].长沙:中南大学,2011.XIE N. The epidemic character of HIV/AIDS and comparison of different prediction models in Shenzhen[D]. Changsha:Central South University,2011.
[10] YU Z C,ZHANG W X,CHEN H,et al. Limited sampling strategy for the estimation of mycophenolic acid area under the plasma concentration-time curve in adult patients undergoing liver transplant[J]. Ther Drug Monit,2007,29(2):207-214. DOI:10. 1097/FTD. 0b013e318040ce0b.
[11] EARNEST A,CHEN M I,NG D,et al. Using autoregressive integrated moving average(ARIMA)models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore[J]. BMC Health Serv Res,2005,5:36. DOI:10. 1186/1472-6963-5-36.
[12]马玉秀. 2006-2015年济南市槐荫区法定传染病流行趋势及预测分析[D].济南:山东大学,2016.MA Y X. The analysis and predicting of trend of notifiable infectious diseases in Huaiyin from 2006-2015[D]. Jinan:Shangdong University,2016.
[13]龚磊,吴家兵,侯赛,等.曲线估计在流行性感冒发病预测中的应用[J].华南预防医学,2017,43(1):30-33. DOI:10. 13217/j. scjpm. 2017. 0030.GONG L,WU J B,HOU S,et al. Application of curvilinear estimation in predicting incidence of influenza[J]. South China J Prev Med,2017,43(1):30-33. DOI:10.13217/j. scjpm. 2017. 0030.
[14]朱宗元,于青. ARIMA模型在我国病毒性肝炎发病率预测中的应用[J].中国卫生统计,2011,28(1):65-67.DOI:10. 3969/j. issn. 1002-3674. 2011. 01. 022.ZHU Z Y,YU Q. Application of ARIMA model in predicting the incidence of viral hepatitis in China[J]. Chin J Health Stat,2011,28(1):65-67. DOI:10. 3969/j. issn.1002-3674. 2011. 01. 022.
[15]赛晓勇,张治英,徐德忠,等.不同时间序列分析法在洞庭湖区血吸虫病发病预测中的比较[J].中华流行病学杂志,2004,25(10):863-866. DOI:10. 3760/j.issn:0254-6450. 2004. 10. 010.SAI X Y,ZHANG Z Y,XU D Z,et al. Application of“time series analysis”in the prediction of schistosomiasis prevalence in areas of “breaking dikes or opening sluice for waterstore”in Dongting Lake areas,China[J]. Chin J Epidemiol,2004,25(10):863-866. DOI:10. 3760/j. issn:0254-6450. 2004. 10. 010.
[16]董蒴,廖小勇,任全玉.艾滋病预测模型及其应用研究-基于灰色系统理论[J].现代商贸工业,2011(8):25-26. DOI:10. 3969/j. issn. 1672-3198. 2011. 08. 015.DONG S,LIAO X Y,REN Q Y. Research on AIDS prediction model and its prediction based on grey system theory[J]. Mod Bus Trade Ind,2011(8):25-26. DOI:10.3969/j. issn. 1672-3198. 2011. 08. 015.