tetano
Editor, Senior Moderator
Zhonghua Liu Xing Bing Xue Za Zhi. 2016 Nov 10;37(11):1491-1496. doi: 10.3760/cma.j.issn.0254-6450.2016.11.012.
[h=1][Research on spatiotemporal spread of avian influenza A(H7N9) virus based on discrete geographic information].[/h] [Article in Chinese]
Wang MX[SUP]1[/SUP], Yuan MQ[SUP]2[/SUP], Fang Y[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Objective: To establish a dynamic spatiotemporal spread modes of influenza A (H7N9) virus by using discrete geographic information and explore the spatiotemporal transmission of the virus. Methods: The gene sequences of H7N9 virus isolated from human in China, which were available from Global Initiative on Sharing Avian Influenza Data (GISAID), were used in alignment by using software BioEdit 7.0. Spatiotemporal spread model of H7N9 virus was established in a Bayesian statistical framework by using software BEAST 1.8.2. The symmetric substitution model and Bayesian stochastic search variable selection (BSSVS) were used to infer and verify the historical transmission route of H7N9 virus. Finally the spatiotemporal transmission route was presented by Google Earth software. Results: The transmission of avian influenza A (H7N9) virus originated in Shanghai and Hangzhou, and can be dated back to October 2012. In March and April 2013, it began to spread to the neighboring provinces. The transmission speed up in August and September and affected more than ten geographic positions within 3 months. Conclusion: Based on gene sequences and spatial geographic information, the transmission route of H7N9 virus was traced, which would support the avian influenza prevention and control as well as avian influenza virus tracing.
PMID: 28057141
[PubMed - in process]
[h=1][Research on spatiotemporal spread of avian influenza A(H7N9) virus based on discrete geographic information].[/h] [Article in Chinese]
Wang MX[SUP]1[/SUP], Yuan MQ[SUP]2[/SUP], Fang Y[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Objective: To establish a dynamic spatiotemporal spread modes of influenza A (H7N9) virus by using discrete geographic information and explore the spatiotemporal transmission of the virus. Methods: The gene sequences of H7N9 virus isolated from human in China, which were available from Global Initiative on Sharing Avian Influenza Data (GISAID), were used in alignment by using software BioEdit 7.0. Spatiotemporal spread model of H7N9 virus was established in a Bayesian statistical framework by using software BEAST 1.8.2. The symmetric substitution model and Bayesian stochastic search variable selection (BSSVS) were used to infer and verify the historical transmission route of H7N9 virus. Finally the spatiotemporal transmission route was presented by Google Earth software. Results: The transmission of avian influenza A (H7N9) virus originated in Shanghai and Hangzhou, and can be dated back to October 2012. In March and April 2013, it began to spread to the neighboring provinces. The transmission speed up in August and September and affected more than ten geographic positions within 3 months. Conclusion: Based on gene sequences and spatial geographic information, the transmission route of H7N9 virus was traced, which would support the avian influenza prevention and control as well as avian influenza virus tracing.
PMID: 28057141
[PubMed - in process]