基于多源地理数据精细尺度的武汉市人居环境新型冠状病毒肺炎疫情传播风险评估

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 基于多源地理数据精细尺度的武汉市人居环境新型冠状病毒肺炎疫情传播风险评估 DOI: 10.5846/stxb202005081143 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学青年基金项目（41801306） Fine-scale risk assessment of COVID-19 in Wuhan based on multisource geographical data Author: Affiliation: Fund Project: National Natural Science Foundation of China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:新型冠状病毒肺炎的迅速传播和扩散警示着疾病风险评估的重要性。但现有的风险评估方法受数据限制，缺少实时性和准确性。此外，多数研究以行政统计单元作为分析尺度，存在可变面元问题。为解决这些问题，耦合精细尺度下武汉市疫情数据及多源地理数据，基于随机森林算法构建社区尺度的市域疫情传播风险评估模型并进行了疫情风险制图。模型测试精度达到0.85，Kappa系数达到0.70。此外，本研究还建立基于随机森林算法的社区及场所尺度的"空间变量-感染风险"模型，评估了不同场所设施疫情传播的风险程度。研究表明，（1）武汉中心区域感染风险最高并呈现出向外围递减的趋势；（2）感染风险排名前五的一级场所类型分别为购物服务、医疗服务、金融服务、交通设施以及公共设施；（3）小学、中学的疫情传播风险较低，而高等院校传播风险较高；（4）社区尺度下的疫情风险程度，预测购物场所与交通场所是疫情传播风险最高的驱动因子。本研究基于精细尺度提出风险评估新方法，可为未来疾病风险评估提供新思路，为疫情防控提供决策支持，人民群众提供安全保障。 Abstract:The severe outbreak of coronavirus disease 2019 (COVID-19) demonstrates the importance of disease risk assessment. The existing risk assessment methods are limited by the real time and accuracy of data. Most of them take the administrative statistical unit as the analysis scale, which has modifiable areal unit problem (MAUP). First, based on a random forest method, we integrated COVID-19 transmission data at community scale and multisource geospatial data to map COVID-19 disease outbreak risks at fine scale. The experimental results (overall accuracy=0.85, Kappa=0.70) indicated the feasibility of the model. Second, we built a spatial variable-infection risk model at community and place scale to assess the risk degree of epidemic spread in different places and facilities. Last, we analyzed the possibly spatial drivers of disease transmission. The results show that (1) the central area of Wuhan city has the highest risk of infection and the risk map presents a trend of decreasing from the center to the periphery; (2) The top five facilities with the highest risk of COVID-19 infection are shopping, medical, financial, transportation and public facilities; (3) The transmission risk of the epidemic is low in primary and middle schools, but high in colleges and universities; (4) The model determines the degree of epidemic risk at the community scale and predicts that shopping and traffic places are two most significant driving factors with the epidemic outbreak. In conclusion, this study suggests a new method of disease risk assessment based on a fine scale, which can pave the way for future disease risk assessment. 参考文献 相似文献 引证文献