Trevon Fuller1, Fiona Havers2, Cuiling Xu3, Li-Qun Fang4, Wu-Chun Cao4, Yuelong Shu3, Marc-Alain Widdowson5, Thomas B Smith6. 1. Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, 619 Charles E. Young Dr. East, Los Angeles, CA 90095, USA. Electronic address: fullertl@ucla.edu. 2. Epidemic Intelligence Service assigned to Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, MS-A04, Atlanta, GA 30333, USA. 3. Chinese National Influenza Center, National Institute for Viral Disease Control and Prevention, China Center for Disease Control and Prevention, 155 Changbai Rd, Changping District, Beijing 102206, People's Republic of China. 4. State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, No. 20, Dongda Street, Fengtai District, Beijing 100071, People's Republic of China. 5. Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, 1600 Clifton Rd NE, MS-A04, Atlanta, GA 30333, USA. 6. Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, 619 Charles E. Young Dr. East, Los Angeles, CA 90095, USA; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. East, Los Angeles, CA 90095, USA.
Abstract
OBJECTIVES: The rapid emergence, spread, and disease severity of avian influenza A (H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future human infections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors. METHODS: This cross-sectional study used the locations of H7N9 cases and negative cases from China's influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia. RESULTS: Live bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus' spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China. CONCLUSIONS: Our risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases.
OBJECTIVES: The rapid emergence, spread, and disease severity of avian influenza A (H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future humaninfections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors. METHODS: This cross-sectional study used the locations of H7N9 cases and negative cases from China's influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia. RESULTS: Live bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus' spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China. CONCLUSIONS: Our risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases.
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