本文選題：計算流行病學 + 復雜網絡�。� 參考：《吉林大學》2017年碩士論文

【摘要】：傳染病早期發(fā)現是計算流行病學和復雜網絡科學的研究熱點。傳染病爆發(fā)具有不可預知、傳播速度快、感染范圍廣和難以有效控制等特點,每次發(fā)生都會給人類社會造成巨大的生命和財產損失。設計有效的傳染病早期發(fā)現方法是傳染病防控的有效手段。盡早的預測出傳染病爆發(fā)的趨勢可為相關部門提供充分的應對時間,提前采取防控措施,將其危害降至最低。現有的傳染病早期發(fā)現研究大都面向靜態(tài)接觸網絡,基于靜態(tài)網絡的拓撲屬性設計早期發(fā)現策略。然而,人與人之間的接觸往往是動態(tài)變化的,靜態(tài)接觸網絡不能很好的刻畫現實世界中的實際接觸行為。靜態(tài)網絡模型與真實接觸模式的偏差會降低早期發(fā)現的準確性。在此背景下,本文開展了面向動態(tài)接觸網絡的傳染病傳播過程建模和早期發(fā)現方法研究,提出3個適用于動態(tài)網絡的早期發(fā)現方法,具體完成如下兩個主要工作。1)借鑒面向動態(tài)網絡的傳染病免疫策略,提出了2種針對動態(tài)接觸網絡的傳染病早期發(fā)現方法,這2種方法都是根據網絡的時序性特點選擇需要重點監(jiān)控的監(jiān)控目標�；谡鎸崝祿瘜λ鼈兊男阅苓M行了實證研究和定量分析。實驗結果表明:借鑒動態(tài)免疫策略提出的早期發(fā)現方法可以實現傳染病的早期發(fā)現工作,并且得到的預測結果較優(yōu)于現有的靜態(tài)網絡下最有效的早期發(fā)現方法。2)以上2種策略在進行早期發(fā)現時需要重復處理接觸數據,計算開銷大。針對該問題,本文改善了以上兩個方法的不足,進一步提出了簡化數據處理的傳染病早期發(fā)現方法。不需要隨機選擇部分個體再進行數據統(tǒng)計,節(jié)省了時間開銷,提高了傳染病的早期發(fā)現能力。該方法可根據易于獲得的數據(局部接觸網絡結構和高活度個體間的接觸時序信息)有效預測傳染病的爆發(fā)時間。基于真實數據集的實證研究表明:該方法得到的預測結果優(yōu)于上述2種基于動態(tài)網絡的早期發(fā)現方法。本文工作是國際上第一個面向動態(tài)接觸網絡的傳染病早期發(fā)現方法研究。該工作進一步完善了現有的面向網絡的傳染病早期發(fā)現方法研究的局限性,為后續(xù)該方向的研究起到了更好的推進作用。
[Abstract]:The early discovery of infectious diseases is a hot topic in computational epidemiology and complex network science. The outbreak of infectious diseases has the characteristics of unpredictable, rapid transmission, wide range of infection and difficult to effectively control, each occurrence will cause huge loss of life and property to human society. To design effective methods for early detection of infectious diseases is an effective means to prevent and control infectious diseases. Early prediction of the trend of infectious disease outbreak can provide adequate response time for relevant departments, take preventive and control measures ahead of time, and reduce its harm to the minimum. Most of the existing researches on early detection of infectious diseases are oriented to static contact networks, and the topology properties of static networks are used to design early detection strategies. However, the contact between people is often dynamic, static contact network can not well describe the actual contact behavior in the real world. The deviation between static network model and real contact mode will reduce the accuracy of early detection. In this context, the modeling and early detection methods of infectious disease transmission process oriented to dynamic contact network are studied in this paper, and three early detection methods suitable for dynamic network are proposed. The following two main works have been accomplished: 1) two methods for early detection of infectious diseases based on dynamic contact network are proposed, which draw lessons from the immunization strategy of infectious diseases oriented to dynamic network. These two methods are based on the temporal characteristics of the network to select monitoring targets that need to be monitored. Based on the real data set, the performance of them is analyzed quantitatively and empirically. The experimental results show that the early detection of infectious diseases can be realized by using the early detection method proposed by the dynamic immune strategy. And the predicted results are better than the most effective early detection methods in static network. 2) the above two strategies need to deal with the contact data repeatedly in the process of early detection, and the computation cost is high. In order to solve this problem, this paper improves the shortcomings of the above two methods, and further proposes an early detection method for infectious diseases which simplifies data processing. No random selection of individuals is needed for data statistics, which saves time and improves the ability of early detection of infectious diseases. This method can effectively predict the outbreak time of infectious diseases based on the easily available data (local contact network structure and contact time series information between individuals with high activity). An empirical study based on real data sets shows that the prediction results obtained by this method are superior to those of the two dynamic network-based early discovery methods. This paper is the first international research on the early detection of infectious diseases for dynamic contact networks. This work further improves the limitations of the existing network oriented early detection methods of infectious diseases, and plays a better role in further research in this direction.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R181;O157.5

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