Title |
Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China
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Published in |
BMC Medicine, May 2014
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DOI | 10.1186/1741-7015-12-88 |
Pubmed ID | |
Authors |
Eric HY Lau, Jiandong Zheng, Tim K Tsang, Qiaohong Liao, Bryan Lewis, John S Brownstein, Sharon Sanders, Jessica Y Wong, Sumiko R Mekaru, Caitlin Rivers, Peng Wu, Hui Jiang, Yu Li, Jianxing Yu, Qian Zhang, Zhaorui Chang, Fengfeng Liu, Zhibin Peng, Gabriel M Leung, Luzhao Feng, Benjamin J Cowling, Hongjie Yu |
Abstract |
Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of 'line lists' with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 45% |
United Kingdom | 3 | 27% |
Netherlands | 1 | 9% |
Unknown | 2 | 18% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 45% |
Practitioners (doctors, other healthcare professionals) | 4 | 36% |
Scientists | 2 | 18% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 4% |
Unknown | 55 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 16% |
Student > Master | 9 | 16% |
Student > Ph. D. Student | 8 | 14% |
Other | 6 | 11% |
Student > Bachelor | 5 | 9% |
Other | 10 | 18% |
Unknown | 10 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 13 | 23% |
Agricultural and Biological Sciences | 8 | 14% |
Computer Science | 5 | 9% |
Biochemistry, Genetics and Molecular Biology | 4 | 7% |
Social Sciences | 3 | 5% |
Other | 14 | 25% |
Unknown | 10 | 18% |