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- Wan Yang
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and
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- Marc Lipsitch
- Center for Communicable Disease Dynamics, Harvard School of Public Health, Harvard University, Boston, MA 02115
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- Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032; and
書誌事項
- 公開日
- 2015-02-17
- 権利情報
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- http://www.pnas.org/site/misc/userlicense.xhtml
- DOI
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- 10.1073/pnas.1415012112
- 公開者
- Proceedings of the National Academy of Sciences
この論文をさがす
説明
<jats:title>Significance</jats:title> <jats:p>Infectious disease surveillance systems are powerful tools for monitoring and understanding infectious disease dynamics; however, underreporting (due to both unreported and asymptomatic infections) and observation errors in these systems create challenges for delineating a complete picture of infectious disease epidemiology. This issue is true for influenza, an infectious disease of pandemic potential. Here we develop and present influenza inference systems capable of compensating for observational biases and underreporting. Using both Google Flu Trends and Centers for Disease Control and Prevention data in conjunction with Bayesian model inference methods, we are able to infer the evolving epidemiological features of influenza and its impacts among the large population during 2003−2013, including the 2009 pandemic. In addition, differences among regions within the United States are identified.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 112 (9), 2723-2728, 2015-02-17
Proceedings of the National Academy of Sciences