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_14
Snippet: Country-wide, state-specific, and spatial analysis of average infection rates To calculate and plot the overall (smooth) development of infection rates with time (c.f., Fig. 2 h) and depending on the weekday (Fig. 2 i) , an appropriate GAMM (structurally strongly related to the above mentioned approach) has been applied. However, data of all ADs have been analysed simultaneously here, using again the federal state as a random intercept, but also .....
Document: Country-wide, state-specific, and spatial analysis of average infection rates To calculate and plot the overall (smooth) development of infection rates with time (c.f., Fig. 2 h) and depending on the weekday (Fig. 2 i) , an appropriate GAMM (structurally strongly related to the above mentioned approach) has been applied. However, data of all ADs have been analysed simultaneously here, using again the federal state as a random intercept, but also the ADs (which are nested within the federal states). Furthermore, we introduced the date again as a smooth term, and the day of the week as a cyclic smooth (c.f., above). Model validation has been performed based on various residual plots (as e.g. suggested in [9, [19] [20] [21] ).
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