Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China
Jean Artois
1, Hui Jiang
1, Xiling Wang, Ying Qin, Morgan Pearcy, Shengjie Lai, Yujing Shi, Juanjuan Zhang, Zhibin Peng, Jiandong Zheng, Yangni He, Madhur S. Dhingra, Sophie von Dobschuetz, Fusheng Guo, Vincent Martin, Wantanee Kalpravidh, Filip Claes, Timothy Robinson, Simon I. Hay, Xiangming Xiao, Luzhao Feng, Sheng Wei
, and Hongjie Yu
Author affiliations: Université Libre de Bruxelles, Brussels, Belgium (J. Artois, M. Pearcy, M.S. Dhingra, M. Gilbert); Chinese Center for Disease Control and Prevention, Beijing, China (H. Jiang, Y. Qin, S. Lai, Y. Shi, Z. Peng, J. Zheng, L. Feng, H. Yu); Fudan University School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China (X. Wang, S. Lai, J. Zhang, Y. He, X. Xiao, H. Yu); Fonds National de la Recherche Scientifique, Brussels, Belgium (M. Pearcy, M. Gilbert); University of Southampton, Southampton, UK (S. Lai); Food and Agriculture Organization of the United Nations, Rome, Italy (M.S. Dhingra, S. von Dobschuetz, T. Robinson); Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific, Bangkok, Thailand (F. Guo, W. Kalpravidh, F. Claes); Food and Agriculture Organization of the United Nations China Office, Beijing (V. Martin); University of Washington, Seattle, Washington, USA (S.I. Hay); University of Oxford, Oxford, UK (S.I. Hay); University of Oklahoma, Norman, Oklahoma, USA (X. Xiao)
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Figure 1
Figure 1. Marginal effect plots of the top 4 predictor variables on the predicted incidence rate of influenza A(H7N9) in China. Change in relative contribution over time is indicated by the bars on the top of each plot, showing the increasing relative contribution of the poultry predictor variables. The smoothed line on the top left part of each plot is indicative of the distribution of each variable.
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