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_52
Snippet: Application to empirical data Since parameter inference was successful for simulated data, the next step was an application to empirical observations. We applied the inference framework to two regional data sets from the RKI data base. As an example, we selected the COVID-19 time series for Köln (RKI data, population size N = 1, 085, 664), which includes 27 days of observations with more than 30 cases and is plotted in Figure 7 . Parameter estim.....
Document: Application to empirical data Since parameter inference was successful for simulated data, the next step was an application to empirical observations. We applied the inference framework to two regional data sets from the RKI data base. As an example, we selected the COVID-19 time series for Köln (RKI data, population size N = 1, 085, 664), which includes 27 days of observations with more than 30 cases and is plotted in Figure 7 . Parameter estimation yields an estimate for the contact rate of β * ≈ 0.7 (Fig. 7b) . Thus, analysis of the negative log-likelihood function produced qualitatively similar results for simulated SEIR time series and empirical data for a representative region. In the main text, we carry out an estimation of the time-resolved instantaneous optimal parameter values β * (t k ) using the instantaneous negative log-likelihood function L(t k , β), Eq. (13).
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