Selected article for: "bootstrap uncertainty and error structure"
Author: Roosa, K.; Lee, Y.; Luo, R.; Kirpich, A.; Rothenberg, R.; Hyman, J.M.; Yan, P.; Chowell, G.
Title: Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020
  • Document date: 2020_2_14
    • ID: 0zw3ukpx_13
    Snippet: We then use a parametric bootstrap approach to quantify uncertainty around the best-fit solution, assuming a Poisson error structure. A detailed description of this method is provided in prior studies (Chowell, 2017; Roosa & Chowell, 2019) . The models are refitted to the M ¼ 200 bootstrap datasets to obtain M parameter sets, which are used to define 95% confidence intervals for each parameter. Each of the M model solutions to the bootstrap curv.....
    Document: We then use a parametric bootstrap approach to quantify uncertainty around the best-fit solution, assuming a Poisson error structure. A detailed description of this method is provided in prior studies (Chowell, 2017; Roosa & Chowell, 2019) . The models are refitted to the M ¼ 200 bootstrap datasets to obtain M parameter sets, which are used to define 95% confidence intervals for each parameter. Each of the M model solutions to the bootstrap curves is used to generate m ¼ 30 simulations extended through a forecasting period of 15 days. These 6000 (M Â m) curves construct the 95% prediction intervals for the forecasts.

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