基于生态位模型的人感染H7N9禽流感病毒潜在风险区预测分析
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摘要
目的利用Maxent生态位模型研究H7N9传染病与环境因素的关系,并在此基础上预测禽流感疫情的潜在风险区域。方法共选择254例江苏省范围内经过临床确诊的H7N9病例作为样本,构建生态位模型,利用ROC曲线下面积(AUC)来验证构建的生态位模型的有效性;同时,利用变量刀切法(Jackknife)来评价各环境变量对H7N9暴发的贡献;在此基础上,利用构建的生态位模型来预测江苏省12月份H7N9传染病暴发的潜在风险区。结果在江苏省境内,温度、日照强度、降水量和NDVI是影响H7N9禽流感疫情暴发的最主要的环境因素;通过分析12月份的潜在风险区预测图可以发现,江苏省苏南地区,尤其是苏州、无锡、常州3市是H7N9疫情暴发的高风险区。结论当江苏省境内出现持续的低温、日照时间短、气温潮湿的天气时,应密切关注禽流感疫情暴发的可能性。
Objective The aim of this study was to explore the use of maximum entropy(Maxent) modelling method to identify the potential risk areas of a disease and the optimal ecological conditions under which the disease is most likely to occur in Jiangsu province. The risk prediction map under specific ecological factors was therefore drawn by projecting the training model across research regions. Methods 254 H7N9 cases were collected in our research. The validity of the model was established by analyzing the area under the curve(AUC). Meanwhile, jackknife tests were applied to enumerate the contribution of different environmental variables to the prediction of the final model. Results The results indicated that the key environmental factors for determining the occurrence of H7N9 in Jiangsu province were temperature, duration of sunshine, precipitation, and NDVI. The potential risk map for the occurrence of H7N9 on December indicated that the south of Jiangsu province, especially the cities of Suzhou, Wuxi and Changzhou were the high-risk areas for the outbreak of H7N9 disease. Conclusion The possibility of the outbreak of H7N9 should be closely concerned when there are sustained low temperature, short sunshine and humid weather in Jiangsu province.
Objective The aim of this study was to explore the use of maximum entropy(Maxent) modelling method to identify the potential risk areas of a disease and the optimal ecological conditions under which the disease is most likely to occur in Jiangsu province. The risk prediction map under specific ecological factors was therefore drawn by projecting the training model across research regions. Methods 254 H7N9 cases were collected in our research. The validity of the model was established by analyzing the area under the curve(AUC). Meanwhile, jackknife tests were applied to enumerate the contribution of different environmental variables to the prediction of the final model. Results The results indicated that the key environmental factors for determining the occurrence of H7N9 in Jiangsu province were temperature, duration of sunshine, precipitation, and NDVI. The potential risk map for the occurrence of H7N9 on December indicated that the south of Jiangsu province, especially the cities of Suzhou, Wuxi and Changzhou were the high-risk areas for the outbreak of H7N9 disease. Conclusion The possibility of the outbreak of H7N9 should be closely concerned when there are sustained low temperature, short sunshine and humid weather in Jiangsu province.
引文
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[13]Liu W,Zhu Y,Qi X,et al.Risk assessment on the epidemics of human infection with a novel avian influenza A(H7N9)virus in Jiangsu Province,China[J].Journal of Biomedical Research,2013,27(3):163-166.
[14]方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析[J].中华流行病学杂志,2005,26(11):839-842.
[15]乔慧捷,胡军华,黄继红.生态位模型的理论基础、发展方向与挑战[J].中国科学(生命科学),2013,43(11):915-927.
[2]Liu W,Yang K,Qi X,et al.Spatial and temporal analysis of human infection with avian influenza A(H7N9)virus in China,2013[J].Eurosurveillance,2013,18(47):31-38.
[3]许可,鲍倡俊.人感染H7N9禽流感流行病学研究进展[J].江苏预防医学,2015,26(1):43-47.
[4]Grinnell J.The Niche-Relationships of the California Thrasher[J].The Auk,1917,34(4):427-433.
[5]Elith J,Leathwick JR.Species distribution models:ecological explanation and prediction across space and time[J].Annual Review of E-cology Evolution and Systematics,2009,40:677-697.
[6]徐继承,黄水平,肖伟伟,等.中国大陆地区438例人感染H7N9禽流感空间聚集性分析[J].中华流行病学杂志,2014,35(11):1270-1274.
[7]Fick SE,Hijmans RJ.World Clim 2:new 1-km spatial resolution climate surfaces for global land areas[J].International Journal of Climatology,2017,37(12):4302-4315.
[8]Hirt C,Filmer MS,Featherstone WE.Comparison and validation of recent freely-available ASTER-GDEM ver1,SRTM ver4.1 and GEODATA DEM-9S ver3 digital elevation models over Australia[J].Australian Journal of Earth Sciences,2010,57(3):337-347.
[9]Friedl MA,Sulla-Menashe D,Tan B,et al.MODIS collection 5global land cover:algorithm refinements and characterization of new datasets[J].Remote Sensing of Environment,2010,114(1):168-182.
[10]Y U,Le,WANG,et al.A multi-resolution global land cover dataset through multisource data aggregation[J].Science China Earth Sciences,2014,57(10):2317-2330.
[11]Phillips SJ,Anderson RP,Schapire RE.Maximum entropy modeling of species geographic distributions[J].Ecological Modelling,2006,190(3/4):231-259.
[12]Phillips SJ,Dudik M.Modeling of species distributions with Maxent:new extensions and a comprehensive evaluation[J].Ecography,2008,31(2):161-175.
[13]Liu W,Zhu Y,Qi X,et al.Risk assessment on the epidemics of human infection with a novel avian influenza A(H7N9)virus in Jiangsu Province,China[J].Journal of Biomedical Research,2013,27(3):163-166.
[14]方立群,曹春香,陈国胜,等.地理信息系统应用于中国大陆高致病性禽流感的空间分布及环境因素分析[J].中华流行病学杂志,2005,26(11):839-842.
[15]乔慧捷,胡军华,黄继红.生态位模型的理论基础、发展方向与挑战[J].中国科学(生命科学),2013,43(11):915-927.