| 顾豪高,张王剑,徐浩,李鹏媛,吴洛林,郭貔,郝元涛,陆家海,张定梅.中国人感染H7N9禽流感危险区域预警识别的研究[J].Chinese journal of Epidemiology,2015,36(5):470-475 |
| 中国人感染H7N9禽流感危险区域预警识别的研究 |
| Predicating risk area of human infection with avian influenza A (H7N9) virus by using early warning model in China |
| Received:October 21, 2014 |
| DOI：10.3760/cma.j.issn.0254-6450.2015.05.013 |
| KeyWord: H7N9禽流感 危险因素 预测 |
| English Key Word: Avian influenza A (H7N9) Risk factors Predicting |
| FundProject:国家自然科学青年基金(81201283); 国家科技重大专项(2012ZX10004-213, 2012ZX10004-902) |
| Author Name | Affiliation | E-mail | | Gu Haogao | Department of Medical Statistic and Epidemiology, School of Public Health | | | Zhang Wangjian | Department of Medical Statistic and Epidemiology, School of Public Health | | | Xu Hao | Department of Medical Statistic and Epidemiology, School of Public Health | | | Li Pengyuan | Department of Medical Statistic and Epidemiology, School of Public Health | | | Wu Luolin | Department of Atmospheric Science, School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou 510080, China | | | Guo Pi | Department of Medical Statistic and Epidemiology, School of Public Health | | | Hao Yuantao | Department of Medical Statistic and Epidemiology, School of Public Health | | | Lu Jiahai | Department of Medical Statistic and Epidemiology, School of Public Health | | | Zhang Dingmei | Department of Medical Statistic and Epidemiology, School of Public Health | zhdingm@mail.sysu.edu.cn |
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| Abstract: |
| 目的 建立人感染H7N9禽流感发病危险预警模型, 识别禽流感暴发高危险区域并提出预警。方法 收集2013年2月至2014年6月中国地市级人感染H7N9禽流感病例数据及同期地理、气象数据, 通过空间自回归(SAR)模型和广义相加模型(GAM)拟合并量化地理和气象因素对发病的影响, 综合两模型的预测结果建立发病危险预警地图。结果 2014年2月中国人感染H7N9禽流感的实际发病地区全部位于研究构建的发病危险预测区域内;模型预测了疾病的空间移动趋势, 对2014年4、5月北方地区的新发疫情有准确的预警。结论 建立的模型短期预测准确度和精确度较好, 可应用于疫情监测和预警领域, 有助于早期区域预防疫情的流行及暴发。 |
| English Abstract: |
| Objective To establish a risk early warning model of human infection with avian influenza A (H7N9) virus and predict the area with high risk of the outbreak of H7N9 virus infection. Methods The incidence data of human infection with H7N9 virus at prefecture level in China from February 2013 to June 2014 were collected, and the geographic and meteorological data during the same period in these areas were collected too. Spatial auto regression (SAR) model and generalized additive model (GAM) were used to estimate different risk factors. Afterwards, the risk area map was created based on the predicted value of both models. Results All the human infections with H7N9 virus occurred in the predicted areas by the early warning model in February 2014. The early warning model successfully predicted the spatial moving trend of the disease, and this trend was verified by two outbreaks in northern China in April and May 2014. Conclusion The established early warning model showed accuracy and precision in short-term prediction, which might be applied in the active surveillance, early warning and prevention/control of the outbreak of human infection with H7N9 virus. |
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