Author: Moritz Mercker; Uwe Betzin; Dennis Wilken
Title: What influences COVID-19 infection rates: A statistical approach to identify promising factors applied to infection data from Germany Document date: 2020_4_17
ID: 09nvausz_16
Snippet: To analyse the average β-values for the different states (Fig. 3 ), in the above mentioned GLMMs (using the normal-respectively the Beta-distribution) the federal state has not been used as a random intercept but as a fixed effect predictor, using the federal state Baden-Württemberg as the baseline level. Furthermore, we neglected the spatial 2D smooth in order to avoid collinearities between the spatial smooth and the federal states. If smooth.....
Document: To analyse the average β-values for the different states (Fig. 3 ), in the above mentioned GLMMs (using the normal-respectively the Beta-distribution) the federal state has not been used as a random intercept but as a fixed effect predictor, using the federal state Baden-Württemberg as the baseline level. Furthermore, we neglected the spatial 2D smooth in order to avoid collinearities between the spatial smooth and the federal states. If smooth spatial maps of the β-values have been generated (Fig. 4) , similar models have been used but now neglecting the federal state variable and using only a spatial 2D smooth as a predictor instead.
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