Selected article for: "model fit and parameter set"
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_12
    Snippet: We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. This process yields the set of model parameters Q that minimizes the sum of squared errors between the model f ðt; QÞ and the data y t ; where Q GLM ¼ (r, p, K), Q Rich ¼ (r, a, K), and Q Sub ¼ (r, p, K 0 , q, C thr ) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter.....
    Document: We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. This process yields the set of model parameters Q that minimizes the sum of squared errors between the model f ðt; QÞ and the data y t ; where Q GLM ¼ (r, p, K), Q Rich ¼ (r, a, K), and Q Sub ¼ (r, p, K 0 , q, C thr ) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter descriptions are provided in the Supplement. Thus, the best-fit solution f ðt; b QÞ is defined by the parameter set b Q ¼ arg min P n t¼1 ðf ðt; QÞ À y t Þ 2 . We fix the initial condition to the first data point.

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