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_13
Snippet: The forward simulations discussed in the previous section demonstrated the predictive power of the SEIR model after sequential data assimilation. In the next step, we generated simulations under two different scenarios. In scenario I, we started with the adapted ensemble of internal model states after data assimilation (April 4th) and iterated the model forward with the mean contact parameter estimated in the week March 29th to April 4th after im.....
Document: The forward simulations discussed in the previous section demonstrated the predictive power of the SEIR model after sequential data assimilation. In the next step, we generated simulations under two different scenarios. In scenario I, we started with the adapted ensemble of internal model states after data assimilation (April 4th) and iterated the model forward with the mean contact parameter estimated in the week March 29th to April 4th after implementation of interventions (Fig. 4 , green area). The simulations smoothly continue the time-course of infected cases for both example regions (Fig. 4a,b) . Daily reported case numbers show a decline for both regions (Fig. 4c,d) .
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