【摘要】：目的:為了探討傳染病動力學(xué)模型、時間序列模型及Richards模型在新疆巴州地區(qū)布魯氏菌病研究與應(yīng)用中的可行性,根據(jù)巴州地區(qū)布魯氏菌病的實(shí)際發(fā)病情況,建立相符的布魯氏菌病傳播流行的動態(tài)模型,掌握巴州地區(qū)布魯氏菌病的總體流行趨勢,為布魯氏菌病的早期預(yù)警及控制,奠定科學(xué)的理論基礎(chǔ),提供可行的參考依據(jù)。方法:首先運(yùn)用季節(jié)指數(shù)驗證巴州地區(qū)布魯氏菌病的季節(jié)性波動規(guī)律,構(gòu)建具有周期性傳播率的動力學(xué)模型,擬合2010~2014年每季度新發(fā)的急性人間布魯氏菌病數(shù)據(jù),MAPE與RMSPE可用于評價模型擬合的效果,并預(yù)測未來人間布魯氏菌病的流行趨勢。通過PRCC方法可提取出模型中對人間布魯氏菌病流行具有統(tǒng)計學(xué)意義的參數(shù),計算出布魯氏菌病傳播的基本再生數(shù)R0,探究參數(shù)改變分別對人間布魯氏菌病流行趨勢及基本再生數(shù)R0敏感性分析,提出防控措施。其次時間序列中的ARIMA乘積季節(jié)模型可用于研究季節(jié)性變化的傳染病發(fā)病規(guī)律,基于巴州地區(qū)2005~2014年間每月新發(fā)的人間布魯氏菌病數(shù)據(jù),建立ARIMA(P,D,Q)(p,d,q)S模型,最優(yōu)模型可通過AIC、SBC及AICC最小值原則選取,根據(jù)最優(yōu)模型擬合及預(yù)測新發(fā)的人間布病流行趨勢。最后針對呈暴發(fā)型特征的人間布魯氏菌病數(shù)據(jù)可運(yùn)用Richards模型分析。模型擬合巴州地區(qū)2013年8月至2014年12月之間累積新發(fā)的人間布魯氏菌病數(shù)量,分析暴發(fā)期間發(fā)病率最高的拐點(diǎn),有助于研究干預(yù)措施相對于拐點(diǎn)的影響,并估計出基本再生數(shù)R0。結(jié)果:首先根據(jù)季節(jié)指數(shù)分析確定新發(fā)的急性人間布魯氏菌病數(shù)量在夏、秋季較高�；诓剪斒暇〉膫鞑C(jī)理分析,構(gòu)建羊/牛及從羊/牛傳播給人的季節(jié)性SEIV動力學(xué)模型,擬合新發(fā)的急性人間布病數(shù)量時MAPE=18.07%,RMSPE=20.89%,說明模擬效果理想,預(yù)測出大約在2023年的夏季新發(fā)的急性人間布魯氏菌病數(shù)量達(dá)到最大值15325(95%CI:11920-18242)。估計出基本再生數(shù)R0=2.5524(95%CI:2.5129-2.6225),表明疫病還將持續(xù)流行,尚不能被消除。參數(shù)的敏感性分析可確定,減少羊/牛出生數(shù)量,增大已感染布魯氏菌病羊/牛屠宰率,增加易感染布魯氏菌病羊/牛免疫接種率,降低免疫接種丟失率為有效控制新發(fā)的急性人間布魯氏菌病流行措施。其次,時間序列模型中原始序列白噪聲檢驗為P0.05,有研究的價值。構(gòu)建的最優(yōu)模型為ARIMA(1,1,1)(0,1,2)12,此時AIC=973.12,SBC=987.02,AICC=973.66最小,模型擬合值與實(shí)際值誤差為MAPE=23.82%,RMSPE=29.64%。根據(jù)已知序列預(yù)測出2015年6月巴州地區(qū)新發(fā)的人間布病數(shù)量達(dá)到最大值91(95%CI:51-131)。最后,Richards模型擬合暴發(fā)時新發(fā)的人間布魯氏菌病數(shù)量誤差為MAPE=6.80%,RMSPE=3.98%,同時估計在2014年6~7月之間人間布魯氏菌病發(fā)病率最高,早于實(shí)施各種防控措施。基本再生數(shù)R0=1.1207(95%CI:0.6091-1.1379),說明人間布魯氏菌病尚不能消除。結(jié)論:具有周期性傳播率的季節(jié)性動力學(xué)模型、ARIMA乘積季節(jié)模型與Richards模型都能較好的模擬出符合各自數(shù)據(jù)特征的人間布魯氏菌病流行趨勢,可行性較高,為相關(guān)工作人員在布魯氏菌病高發(fā)季之前做好預(yù)防準(zhǔn)備,提供防控參考策略。
[Abstract]:OBJECTIVE: To explore the feasibility of infectious disease dynamics model, time series model and Richards model in the study and application of Brucellosis in Bazhou, Xinjiang. According to the actual incidence of Brucellosis in Bazhou, a corresponding dynamic model of Brucellosis transmission and epidemic was established to master the total Brucellosis in Bazhou. Methods: Firstly, the seasonal fluctuation of Brucellosis in Bazhou was verified by seasonal index, and a dynamic model with periodic transmission rate was constructed to fit the new acute human cases from 2010 to 2014. MAPE and RMSPE can be used to evaluate the fitting effect of the model and predict the epidemic trend of human brucellosis in the future. The parameters with statistical significance for human brucellosis epidemic in the model can be extracted by PRCC method, and the basic reproduction number R0 of brucellosis transmission can be calculated, and the change of parameters can be explored respectively. Secondly, the ARIMA product seasonal model in time series can be used to study the seasonal variation of the incidence of infectious diseases. Based on the new human Brucellosis data from 2005 to 2014, the ARIMA (P, D, Q) (p, d, q) S model is established. The optimal model can be selected by AIC, SBC and AICC minimum principle, and then fitted and predicted the epidemic trend of new human brucellosis according to the optimal model. Results: First, the number of new acute human brucellosis in summer and autumn was determined according to the seasonal index analysis. Based on the analysis of the transmission mechanism of brucellosis, the construction was made. The seasonal SEIV kinetic model of sheep/cattle and human transmission from sheep/cattle fitted the number of new acute human brucellosis with MAPE=18.07% and RMSPE=20.89%, indicating that the simulation effect was satisfactory. The maximum number of new acute human brucellosis in summer of about 2023 was predicted to reach 15325 (95% CI: 11920-18242). 2.5524 (95% CI: 2.5129-2.6225), indicating that the epidemic will continue to prevail and can not be eliminated. Sensitivity analysis of the parameters can be determined to reduce the number of sheep/cattle born, increase the slaughter rate of infected sheep/cattle, increase the immune inoculation rate of susceptible to brucellosis sheep/cattle, and reduce the loss of immune inoculation rate to effectively control new acute people. Secondly, the white noise test of the original sequence in the time series model is P 0.05, which is of research value. The optimal model is ARIMA (1,1,1) (0,1,2) 12. At this time, AIC = 973.12, SBC = 987.02, AICC = 973.66 is the smallest. The error between the fitting value and the actual value of the model is MAPE = 23.82%, RMSPE = 29.64%. The maximum number of new human brucellosis was 91 (95% CI: 51-131) in Bazhou in June, 2004. Finally, the error of fitting the Richards model to the outbreak was MAPE = 6.80%, RMSPE = 3.98%, and the incidence of human brucellosis was estimated to be the highest between June and July, 2014, earlier than the implementation of various control measures. R0 = 1.1207 (95% CI: 0.6091-1.1379), indicating that human brucellosis can not be eliminated. Conclusion: Seasonal dynamics model with periodic transmission rate, ARIMA product seasonal model and Richards model can better simulate the epidemic trend of human brucellosis in accordance with their respective data characteristics, the feasibility is high, for the relevant staff in Brucellosis prepares for prevention before the high season, and provides a reference strategy for prevention and control.
【學(xué)位授予單位】：新疆醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R516.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李玉華;;布魯氏菌病疫情監(jiān)測與控制分析[J];世界最新醫(yī)學(xué)信息文摘;2016年59期

2 楊建東;樊旭成;秦麗巖;芮寶玲;;SARIMA模型在手足口病發(fā)病率監(jiān)測中的應(yīng)用[J];公共衛(wèi)生與預(yù)防醫(yī)學(xué);2016年01期

3 李妍;孫向東;劉平;彭民金;宇傳華;;中國布魯氏菌病發(fā)病率的系統(tǒng)性評價[J];公共衛(wèi)生與預(yù)防醫(yī)學(xué);2016年01期

4 李愛巧;王濤;吳星星;劉剡;楊小亮;王文秀;何生;周棕長;施遠(yuǎn)翔;;新疆烏魯木齊市流通環(huán)節(jié)牛羊布魯氏菌病的調(diào)查分析[J];中國動物檢疫;2016年02期

5 鄭彥玲;張利萍;張學(xué)良;鄭艷梅;鄭玉建;;SARIMA模型用于新疆乙肝發(fā)病率預(yù)測的探討[J];現(xiàn)代預(yù)防醫(yī)學(xué);2015年22期

6 王微;殷菲;靳圓圓;曹明芹;;新疆布魯氏菌病空間聚集性分析[J];中國人獸共患病學(xué)報;2015年10期

7 王鳳;;季節(jié)變動分析方法在生產(chǎn)中的應(yīng)用[J];銅業(yè)工程;2015年05期

8 李明明;王旭皓;孫舒曼;李智明;;基于ARIMA與季節(jié)指數(shù)組合模型的居民儲蓄存款預(yù)測[J];經(jīng)濟(jì)研究導(dǎo)刊;2015年05期

9 木合塔爾·艾山;何海波;邰新平;陳俠;童蘇祥;王忠;;新疆2013年人間布魯氏菌病監(jiān)測結(jié)果及疫情分析[J];中國媒介生物學(xué)及控制雜志;2015年01期

10 高彥輝;趙麗軍;孫殿軍;顧愛華;張作文;董爾丹;;布魯氏菌病防治基礎(chǔ)研究現(xiàn)狀與展望[J];中國科學(xué):生命科學(xué);2014年06期

，

本文編號：2211196