Selected article for: "daily number and GAM model"
Author: Tao Liu; Jianxiong Hu; Jianpeng Xiao; Guanhao He; Min Kang; Zuhua Rong; Lifeng Lin; Haojie Zhong; Qiong Huang; Aiping Deng; Weilin Zeng; Xiaohua Tan; Siqing Zeng; Zhihua Zhu; Jiansen Li; Dexin Gong; Donghua Wan; Shaowei Chen; Lingchuan Guo; Yan Li; Limei Sun; Wenjia Liang; Tie Song; Jianfeng He; Wenjun Ma
Title: Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China
  • Document date: 2020_1_26
    • ID: 3e2soc6w_7
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.01.25.919787 doi: bioRxiv preprint 6 to predict the daily number of incidences based on the daily number of reporting cases in other provinces of China, which was added up to obtain the daily incidences nationwide. In order to test the validity of this approach, we randomly divided all cases in Guangdong into two groups (group A and B). Case.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.01.25.919787 doi: bioRxiv preprint 6 to predict the daily number of incidences based on the daily number of reporting cases in other provinces of China, which was added up to obtain the daily incidences nationwide. In order to test the validity of this approach, we randomly divided all cases in Guangdong into two groups (group A and B). Cases in group A was used to establish a GAM model, which was then used to predict the number of incidences in group B. We then estimated the correlation between the actual incidence and the estimated incidence, and the results of cross-validation showed that the R 2 was 89.7%, and the root mean square error (RMSE) was 4.11 ( Figure S1 ).

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