交通分析区尺度上的COVID-19时空扩散推估方法:以武汉市为例

被引:25
作者
冯明翔 [1 ]
方志祥 [1 ]
路雄博 [2 ]
谢泽丰 [2 ]
熊盛武 [2 ]
郑猛 [3 ]
黄守倩 [1 ]
机构
[1] 武汉大学测绘遥感信息工程国家重点实验室
[2] 武汉理工大学计算机科学与技术学院
[3] 武汉市交通发展战略研究院
基金
国家重点研发计划;
关键词
交通分析区; 新型冠状病毒肺炎; 流行病学模型; 人群动态; 空间交互; 手机数据;
D O I
10.13203/j.whugis20200141
中图分类号
R181.3 [流行病学各论]; R563.1 [肺炎]; U12 [城市交通运输];
学科分类号
摘要
现有的流行病学模型大多是通过对统计数据进行拟合,实现对患病人数的推估,较少考虑细粒度空间人群移动交互对时空扩散特征的直接作用。将空间交互特征融入流行病学模型,提出了基于手机用户空间交互数据的新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)时空扩散推估方法,并对2019-12—2020-03武汉市COVID-19患病人数以及时空扩散过程进行推估。研究结果表明,该方法能有效推估出每天的疫情新增交通分析区,能够完全覆盖了有疫情公告的交通分析区,且存在疫情公告的交通分析区占所推估交通分析区的72.7%;2020-02-18后的累计推估患者结果与官方公布患者总量吻合得非常好,差距约为5.6%,间接验证了前期推估的合理性。由此得出,该方法能比较有效地推估细粒度空间之间的传染病传播,对正确认识细粒度空间下人群交互对传染病时空扩散的影响机制,增强宏观流行病学模型的空间可解释性具有一定的科学意义。
引用
收藏
页码:651 / 657+681 +681
页数:8
相关论文
共 12 条
  • [1] 新型冠状病毒肺炎基本再生数的初步预测
    周涛
    刘权辉
    杨紫陌
    廖敬仪
    杨可心
    白薇
    吕欣
    张伟
    [J]. 中国循证医学杂志, 2020, (03) : 359 - 364
  • [2] Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China
    Wang, Huwen
    Wang, Zezhou
    Dong, Yinqiao
    Chang, Ruijie
    Xu, Chen
    Yu, Xiaoyue
    Zhang, Shuxian
    Tsamlag, Lhakpa
    Shang, Meili
    Huang, Jinyan
    Wang, Ying
    Xu, Gang
    Shen, Tian
    Zhang, Xinxin
    Cai, Yong
    [J]. CELL DISCOVERY, 2020, 6 (01)
  • [3] Why does the coronavirus spread so easily between people?.[J].Mallapaty Smriti.Nature.2020, 7798
  • [4] Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
    Wrapp, Daniel
    Wang, Nianshuang
    Corbett, Kizzmekia S.
    Goldsmith, Jory A.
    Hsieh, Ching-Lin
    Abiona, Olubukola
    Graham, Barney S.
    McLellan, Jason S.
    [J]. SCIENCE, 2020, 367 (6483) : 1260 - +
  • [5] Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan; China: a descriptive study.[J].Nanshan Chen;Min Zhou;Xuan Dong;Jieming Qu;Fengyun Gong;Yang Han;Yang Qiu;Jingli Wang;Ying Liu;Yuan Wei;Jia'an Xia;Ting Yu;Xinxin Zhang;Li Zhang.The Lancet.2020, prepublish
  • [6] Clinical features of patients infected with 2019 novel coronavirus in Wuhan; China.[J].Chaolin Huang;Yeming Wang;Xingwang Li;Lili Ren;Jianping Zhao;Yi Hu;Li Zhang;Guohui Fan;Jiuyang Xu;Xiaoying Gu;Zhenshun Cheng;Ting Yu;Jiaan Xia;Yuan Wei;Wenjuan Wu;Xuelei Xie;Wen Yin;Hui Li;Min Liu;Yan Xiao;Hong Gao;Li Guo;Jungang Xie;Guangfa Wang;Rongmeng Jiang;Zhancheng Gao;Qi Jin;Jianwei Wang;Bin Cao.The Lancet.2020, prepublish
  • [7] Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions
    Yang, Zifeng
    Zeng, Zhiqi
    Wang, Ke
    Wong, Sook-San
    Liang, Wenhua
    Zanin, Mark
    Liu, Peng
    Cao, Xudong
    Gao, Zhongqiang
    Mai, Zhitong
    Liang, Jingyi
    Liu, Xiaoqing
    Li, Shiyue
    Li, Yimin
    Ye, Feng
    Guan, Weijie
    Yang, Yifan
    Li, Fei
    Luo, Shengmei
    Xie, Yuqi
    Liu, Bin
    Wang, Zhoulang
    Zhang, Shaobo
    Wang, Yaonan
    Zhong, Nanshan
    He, Jianxing
    [J]. JOURNAL OF THORACIC DISEASE, 2020, 12 (03) : 165 - +
  • [8] Big city, small world: density, contact rates, and transmission of dengue across Pakistan
    Kraemer, M. U. G.
    Perkins, T. A.
    Cummings, D. A. T.
    Zakar, R.
    Hay, S. I.
    Smith, D. L.
    Reiner, R. C., Jr.
    [J]. JOURNAL OF THE ROYAL SOCIETY INTERFACE, 2015, 12 (111)
  • [9] The scaling of contact rates with population density for the infectious disease models
    Hu, Hao
    Nigmatulina, Karima
    Eckhoff, Philip
    [J]. MATHEMATICAL BIOSCIENCES, 2013, 244 (02) : 125 - 134
  • [10] A compartmental model for the analysis of SARS transmission patterns and outbreak control measures in China.[J].Juan Zhang;Jie Lou;Zhien Ma;Jianhong Wu.Applied Mathematics and Computation.2004, 2