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Timely and effective surveillance is critical for the prevention and control of epidemics. However, due to technical challenges and shortage of human resources, comprehensive and timely data collection required for effective surveillance, especially collection of data about sudden epidemic outbreaks, is still very difficult. In this paper, we propose the use of multi-source web data for epidemic surveillance. We use the 2009 Influenza A (H1N1) pandemic in Beijing as a case study to demonstrate the utility of our proposed approach. Experiments using data from the Beijing Center for Disease Control and Prevention (CDC) and several search engines show encouraging results. This case study also has direct practical values in the real setting.