Selected article for: "confidence interval and observed number"
Author: Jiawen Hou; Jie Hong; Boyun Ji; Bowen Dong; Yue Chen; Michael P Ward; Wei Tu; Zhen Jin; Jian Hu; Qing Su; Wenge Wang; Zheng Zhao; Shuang Xiao; Jiaqi Huang; Wei Lin; Zhijie Zhang
Title: Changing transmission dynamics of COVID-19 in China: a nationwide population-based piecewise mathematical modelling study
  • Document date: 2020_3_30
    • ID: kuf8p4e1_23
    Snippet: The key parameters in the above models were estimated using MATLAB R2018b through the least square technique. The 95% confidence interval (95% CI) of the parameter was estimated by independently adding random terms to the initial parameter value during the fitting process. The models for different provinces were calibrated separately. To assess the goodness-of-fit of the models, we calculated correlation coefficients between simulated and observe.....
    Document: The key parameters in the above models were estimated using MATLAB R2018b through the least square technique. The 95% confidence interval (95% CI) of the parameter was estimated by independently adding random terms to the initial parameter value during the fitting process. The models for different provinces were calibrated separately. To assess the goodness-of-fit of the models, we calculated correlation coefficients between simulated and observed data, i.e., between model-predicted cumulative number of infections and the reported cumulative

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