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| Evaluation of the H7N9 Transmission Model Predicted by Big Data by Phylogenetic Tree |
| DU Peng-cheng1,2, YU Wei-wen1,2, CHEN Yu-bao3, YAN Peng-cheng3, AN Yun-he4, CHEN Chen1,2 |
1. State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
2. Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China;
3. Beijing Computing Center, Beijing 100094, China;
4. Beijing Centre for Physical & Chemical Analysis, Beijing 100089, China |
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Abstract It has been widely proved that the emergence and transmission of H7N9 avian influenza in China 2013 is closely associated with the live poultry trading all over the mainland China. By analyzing the poultry trading information in mainland China from websites using big data analysis technology, we could perform the work of H7N9 outbreak tracing,transmission analysis and trend prediction. In current study, we obtained the nucleotide sequences of hemagglutinin gene of H7N9 avian influenza virus isolates collected before 2013 from the Influenza Research Database firstly, and then conducted phylogenetic analysis using maximum likelihood method by RAxML software. A phylogenetic tree was constructed and then based on the phylogenetic relationship and isolate background including sources, time and locations of collection, we built several transmission routes of H7N9 between provinces and cities during the outbreak emerged in China in the first half of 2013. The results from phylogenetic analysis were compared with the ones from big data analysis to modify the transmission model which has been built by the big data method in our previous research. It revealed that the phylogenetictree could provide more accurate transmission information to trace the real route which could be used to complete and modify the model from big data, but the connection and transmission model from big data analysis could provide nearly accurate information from larger areas in time, which is meaningful in facing the H7N9 outbreak and could provide valuable basis for setting up and carrying out health policy and measures of disease control and prevention.
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Received: 20 September 2014
Published: 25 November 2014
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