Selected article for: "data analysis and model fitting"
Author: Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams
Title: Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale
  • Document date: 2020_4_18
    • ID: j5o8it22_12
    Snippet: All data analysis was done using R statistical software, version 3.6.2. As described in Churches [25] , we used the ode() default solver from the desolve package to solve the system of ODEs defining the SIR model. Next, we used a quasi-Newton method with constraints to find the optimal values for β and γ on (0, 1) by minimizing the square root of the sum of the squared differences between I, which is our prevalence, and its prediction , over al.....
    Document: All data analysis was done using R statistical software, version 3.6.2. As described in Churches [25] , we used the ode() default solver from the desolve package to solve the system of ODEs defining the SIR model. Next, we used a quasi-Newton method with constraints to find the optimal values for β and γ on (0, 1) by minimizing the square root of the sum of the squared differences between I, which is our prevalence, and its prediction , over all time t [26] . In order to establish initial conditions for model fitting, we estimate the population size of NC and the CRI to be 10,488,084 and 2,544,041, respectively, using information taken from census estimates. After obtaining the estimatesand ., to help assess model goodness-offit, we define the following statistic:

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