Participants in our research group

    本课题组参与人员：

       尹凌、  刘康、  张浩、  奚桂锴、  任倩茹、  李子垠、  李璇、  薛建章

Keywords

    关键词:

       Epidemic modeling, prevention and control strategy, risk assessment, urban area, trajectory data, COVID-19, dengue fever, influenza

Description

    简介:

   • Research on new framework and methodology for modeling and simulating infectious diseases based on multi-source trajectory data. 基于多源轨迹数据研究城市尺度上新的传染病扩散建模与模拟方法，以促进城市内部传染病疫情的有效预测及控制。
       • Research on prevention and control strategies of infectious diseases based on large-scale individual trajectory data. 融合大规模个体轨迹数据，研究传染病传播模拟和时空防控策略。
       • Research on epidemic computing models based on large scale mobile sensing data. 研究基于大规模移动感知数据的传染病计算模型。

Publication

    论文:

Ze-Yu Zhao, Yuan-Zhao Zhu, Jing-Wen Xu, Shi-Xiong Hu, Qing-Qing Hu, Zhao Lei, Jia Rui, Xing-Chun Liu, Yao Wang, Meng Yang, Li Luo, Shan-Shan Yu, Jia Li, Ruo-Yun Liu, Fang Xie, Ying-Ying Su, Yi-Chen Chiang, Ben-Hua Zhao, Jing-An Cui, Ling Yin, Yan-Hua Su, Qing-Long Zhao, Li-Dong Gao & Tian-Mu Chen. "A five-compartment model of age-specific transmissibility of SARS-CoV-2." Infectious diseases of poverty 9.1 (2020): 1-15.(SCI)

Chen, Tian-Mu, Jia Rui, Qiu-Peng Wang, Ze-Yu Zhao, Jing-An Cui, and Ling Yin. "A mathematical model for simulating the phase-based transmissibility of a novel coronavirus." Infectious diseases of poverty 9.1 (2020): 1-8.(SCI)

Ling Yin, Nan Lin, Xiaoqing Song, Shujiang Mei, Shih-Lung Shaw, Zhixiang Fang, Qinglan Li, Ye Li, Liang Mao. Space-Time Personalized Short Message Service (SMS) for Infectious Disease Control – Policies for Precise Public Health. Applied Geography (SSCI)

周志峰，李学云，廖玉学，尹淩，许玉成，梁静，梅树江，2019. 前瞻性时空重排扫描统计量法在深圳市MUMPS聚集性疫情早期预警中的应用，实用预防医学(CSCD)(In press)

Kang Liu, Ling Yin*, Zhanwu Ma, Fan Zhang, Juanjuan Zhao, 2019. Investigating Physical Encounters of Individuals in Urban Metro Systems by Using Large-Scale Smart Card Data. Physica A : Statistical Mechanics and its Applications. (SCI)

Qiao Wan, Ling Yin*, Liang Mao, Li Wang, Shujiang Mei, Qinglan Li, Kang Liu, 2019. Simulating human host interventions to control intra-urban dengue outbreaks with a spatially individual-based model. In Proceedings of the IEEE International Conference on Real-time Computing and Robotics. (EI)

Guikai Xi, Ling Yin, Ye Li, and Shujiang Mei, 2018. A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale. In 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI’18), November 6, 2018, Seattle, WA, USA. (EI)

Liang Mao, Ling Yin, Xiaoqing Song, Shujiang Mei, 2016. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data. Acta Tropica, 162:188-195. (SCI)

 

Predicted local acquisition risk of dengue fever in Shenzhen city at a 100 m resolution, produced from the random forest classification model.		Coverage areas with top 50 importation risk and top 100 uncertainty to inform policy making.

Cooperations

    合作单位：

	Department of Geography, University of Florida, Gainesville, FL, USA 美国佛罗里达大学			State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University, Wuhan, China 武汉大学测绘遥感信息工程国家重点实验室			Shenzhen Center for Disease Control and Prevention, Shenzhen, China 深圳市疾病预防控制中心