Selected article for: "ensemble Kalman filter and Kalman filter"
Author: Ralf Engbert; Maximilian M. Rabe; Reinhold Kliegl; Sebastian Reich
Title: Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
  • Document date: 2020_4_17
    • ID: 855am0mv_41
    Snippet: i.e., H = (0, 0, 1, 1). As initial condition, we set I 1 as the number of infected cases, R 1 = 0, so that y obs (t 0 ) = I 1 +R 1 , and E 1 = g/a·I 1 with additive noise. We assume that the errors in the observed y obs (t k ) is additive Gaussian with mean zero and variance ρ. We set ρ = 10 in our experiments. The analysis step of the ensemble Kalman filter is now based on the empirical mean.....
    Document: i.e., H = (0, 0, 1, 1). As initial condition, we set I 1 as the number of infected cases, R 1 = 0, so that y obs (t 0 ) = I 1 +R 1 , and E 1 = g/a·I 1 with additive noise. We assume that the errors in the observed y obs (t k ) is additive Gaussian with mean zero and variance ρ. We set ρ = 10 in our experiments. The analysis step of the ensemble Kalman filter is now based on the empirical mean

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