| 珠江三角洲地区登革热流行风险空间模拟与预测 |
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| 引用本文： | 郑斓,任红艳,施润和,鲁亮.珠江三角洲地区登革热流行风险空间模拟与预测[J].地球信息科学,2019,21(3):407-416. |
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| 作者姓名： | 郑斓 任红艳 施润和 鲁亮 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 1001012. 华东师范大学地理信息科学教育部重点实验室,上海 2002413. 华东师范大学地理科学学院,上海 2002414. 华东师范大学环境遥感与数据同化联合实验室,上海 2002415. 中国疾病预防控制中心传染病预防控制所,北京 102206 |
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| 基金项目： | 国家自然科学基金项目（41571158）;资源与环境信息系统国家重点实验室自主创新项目（O8R8B6A0YA）;国家重点研究发展计划（2016YFC1201305）;国家重点研究发展计划（2016YFC1302602）;上海市卫计委重点学科建设项目（15GWZK0201） |
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| 摘 要： | 近年来日益严重的登革热疫情已在中国南部地区形成疫情高发区,并对中国的公共卫生安全形成了一定的威胁。登革热主要受到区域内复杂的自然环境条件以及社会经济因素的影响,而利用地理空间分析方法和模型探究登革热疫情的影响因素,并对其未来流行风险的空间分布进行模拟,是有效开展登革热预防控制工作的重要基础。本文收集了珠江三角洲地区2010-2014年的登革热病例资料和土地利用、人口密度两种社会经济要素数据,构建土地利用回归（LUR）模型以分析登革热疫情与不同空间范围内的土地利用和人口密度之间的关系,并结合SLEUTH模型获取的2030年土地利用数据以及基于人口密度预测模型获取的2030年人口密度数据,预测珠江三角洲地区2030年登革热疫情风险的空间分布。结果表明,社会经济要素对登革热疫情空间分布的影响在不同范围内存在差异,半径分别为10、7、10、2和1 km的缓冲区内的人口密度、草地、城镇用地、林地和耕地进入LUR模型并对疫情有显著的影响（相关系数分别为0.779、-0.473、0.818、-0.642和-0.403）,所构建的LUR模型效果较好（调整R2为0.796,F=390.409,P<0.01）,留一交叉检验结果显示模型的相对均方根误差为0.7046,预测值与实测值的拟合精度达到0.7101。2030年城市空间扩展的区域主要分布在深圳、东莞以及广佛的交界地区,而登革热风险预测模型表明2030年登革热疫情风险较大的区域与珠江三角洲城镇用地占比、人口分布较高的地区有高度的一致性,尤其是广佛地区。因此,LUR模型可以较好地预测登革热疫情的空间分布,从而为当地卫生部门防控登革热提供方法支持。
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| 关 键 词： | 登革热 空间分布 预测 LUR模型 SLEUTH模型 珠江三角洲地区 |
| 收稿时间： | 2018-05-19 |
Spatial Simulation and Prediction of Dengue Fever Transmission in Pearl River Delta |
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| Lan ZHENG,Hongyan REN,Runhe SHI,Liang LU.Spatial Simulation and Prediction of Dengue Fever Transmission in Pearl River Delta[J].Geo-information Science,2019,21(3):407-416. |
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| Authors: | Lan ZHENG Hongyan REN Runhe SHI Liang LU |
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| Abstract: | Dengue fever (DF) is a rapidly spreading vector-borne viral disease that is widely prevalent in some tropical and subtropical regions. In recent years, the increasingly serious DF epidemic has formed a high incidence area in southern China and has posed a definite threat to China's public health security. DF is mainly affected by the complex environmental conditions and socio-economic factors in the region. Thus, exploring the influencing factors on the spatial distribution of DF by spatial geographic models and predicting the prevalence of DF epidemic are important bases for effective prevention and control of DF. Base on the socioeconomic data (such as land use data of urban, village, forest, farmland, grass, wetland, water area, construction area and population density data) and the spatial data of DF cases from 2010 to 2014 in the Pearl River Delta area (PRD), the Land Use Regression (LUR) model was constructed to analyze the impact of social and economic factors on the spatial distribution of DF epidemic within a range of 1~10 km buffer zones using 500 sample points. In addition, the land use data in 2030 predicted by the SLEUTH model, and the population density in 2030 obtained from the population prediction model were collected to reveal the risk of the DF epidemic in 2030. The results found that DF was significantly correlated with population density (R2=0.779), grass (R2= -0.473), urban (R2=0.818), forest (R2=-0.642) and farmland (R2=-0.403) within the buffer zone of 10 km, 7 km, 10 km, 2 km and 1km respectively. The LUR model with these five variables possessed the satisfactory capability of predicting the spatial distribution of DF with the adjusted R2=0.796 and an appropriate F value of 390.409 (P<0.01). The overall result of the model is good with the fitting accuracy of 0.7101 between the predicted values and the measured values. And the leave-one cross test results show that the model has a relative root mean square error of 0.7046. Further more, the accuracy of land use data in 2030 simulated by the SLEUTH model is good (the kappa coefficient is 0.849) as well with the expanded urban areas. They are mainly distributed in Shenzhen, Dongguan and the border areas of Guangzhou-Foshan, which also have relatively high risk of DF epidemic in 2030. In summary, LUR model can predict the spatial distribution of DF epidemics with high accuracy, which provides supports in methodology for local hygienic authorities to prevent and control DF epidemics. |
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| Keywords: | dengue fever spatial distribution prediction land use regression model SLEUTH model Pearl River Delta |
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