Infectious diseases constitute an important source of safety threats to public health. In the current context of globalization and urbanization, population density in urban areas is rising, human interactions are becoming increasingly frequent, the world is getting smaller, and the risk of sudden epidemics is ever growing, thus raising new challenges to the prevention and control of abrupt infectious diseases. Mastering patterns of the spread and diffusion of infectious diseases, topological characteristics of disease spread networks, development trends of epidemic situations, and so on can provide a scientific basis for the design of effective emergency response plans. In this thesis, with the 2003 SARS epidemic and the 2009 Influenza A(H1N1) in Beijing as research objects, we investigate network construction and missing relationship inference, network analysis, modeling of multiple open source-based surveillance, and social computing-based approach to prevention and control strategies. More specifically, the main contents of the thesis are as follows. 1. We study the spread network of the 2003 Beijing SARS outbreak using the means of complex networks. We infer missing relationships and detect community structures from this network. As is rare in related research, we conduct an empirical study of the Beijing SARS spread network. 2. We advocate multiple open source-based surveillance of epidemic situations. The timely collection of data about epidemic situations is very important for epidemic prevention and control. However, traditional data collection methods do not allow timely effective collection of data about the progress of sudden epidemics. Using the A H1N1 influenza activity in Beijing as a case, we construct a plan for a surveillance system. Experimental results show that this method is more timely and effective than traditional ones. The main contributions of the thesis include a comprehensive analysis of the Beijing SARS spread network, modeling of the prediction of disease outbreaks, and a novel method for disease prevention and control. The research outcomes have important implications to timely and effective emergency response against potential future acute infectious disease epidemics. Our methods provide exemplars to the study of similar epidemics.
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