应用预防医学
應用預防醫學
응용예방의학
JOURNAL OF APPLIED PREVENTIVE MEDICINE
2015年
3期
144-147
,共4页
王丹霞%林伟%饶正远%何金戈%夏勇%李婷%李运葵
王丹霞%林偉%饒正遠%何金戈%夏勇%李婷%李運葵
왕단하%림위%요정원%하금과%하용%리정%리운규
灰色模型%肺结核%预测
灰色模型%肺結覈%預測
회색모형%폐결핵%예측
grey model%tuberculosis%prediction
目的:评价用灰色模型预测四川省网络直报系统报告发病率和结核病专报系统患者登记率的科学性,为四川省结核病疫情预测提供依据。方法根据2006—2014年四川省网络直报系统的报告发病率和结核病专报系统的患者登记率,建立各自的灰色模型,并对2015年和2016年四川省肺结核的监测疫情进行预测。结果建立的网络直报系统报告发病率的灰色模型后验差比值C为0.204,小误差概率P为1,属“好”一级;结核病专报系统患者登记率的灰色模型后验差比值C为0.130,小误差概率P为1,也属“好”一级。结论灰色模型能较好的拟合四川省两个肺结核监测系统监测的流行趋势,预测结果具有一定的参考价值,能为相关部门及时了解和预测结核疫情提供科学依据。
目的:評價用灰色模型預測四川省網絡直報繫統報告髮病率和結覈病專報繫統患者登記率的科學性,為四川省結覈病疫情預測提供依據。方法根據2006—2014年四川省網絡直報繫統的報告髮病率和結覈病專報繫統的患者登記率,建立各自的灰色模型,併對2015年和2016年四川省肺結覈的鑑測疫情進行預測。結果建立的網絡直報繫統報告髮病率的灰色模型後驗差比值C為0.204,小誤差概率P為1,屬“好”一級;結覈病專報繫統患者登記率的灰色模型後驗差比值C為0.130,小誤差概率P為1,也屬“好”一級。結論灰色模型能較好的擬閤四川省兩箇肺結覈鑑測繫統鑑測的流行趨勢,預測結果具有一定的參攷價值,能為相關部門及時瞭解和預測結覈疫情提供科學依據。
목적:평개용회색모형예측사천성망락직보계통보고발병솔화결핵병전보계통환자등기솔적과학성,위사천성결핵병역정예측제공의거。방법근거2006—2014년사천성망락직보계통적보고발병솔화결핵병전보계통적환자등기솔,건립각자적회색모형,병대2015년화2016년사천성폐결핵적감측역정진행예측。결과건립적망락직보계통보고발병솔적회색모형후험차비치C위0.204,소오차개솔P위1,속“호”일급;결핵병전보계통환자등기솔적회색모형후험차비치C위0.130,소오차개솔P위1,야속“호”일급。결론회색모형능교호적의합사천성량개폐결핵감측계통감측적류행추세,예측결과구유일정적삼고개치,능위상관부문급시료해화예측결핵역정제공과학의거。
Objective To evaluate the applicability of grey model in predicting epidemic trends of TB in Sicuan Province based on the data from the National Notifiable Disease Surveillance System(direct reporting system) and special report system for tuberculosis. Methods The incidence data of TB in Sichuan Province between 2006-2014 from direct reporting system and special reporting system for TB were used to establish gray model and forecast tuberculosis epidemic in 2015 and 2016. Results Both gray models established based on data from direct reporting system or from special report system for TB were evaluated as "good" with the posteriori variance ratio C of 0.204 and 0.130 respectively,and both models had the small error probability P of 1.Conclusion Gray model can fit TB epidemic trends perfectly in two tuberculosis surveillance systems in Sichuan, offering a certain reference values for epidemic predicting and decision-making of relevant TB control and prevention.