Author: Janik Schuttler; Reinhard Schlickeiser; Frank Schlickeiser; Martin Kroger
Title: Covid-19 predictions using a Gauss model, based on data from April 2 Document date: 2020_4_11
ID: 14x9luqu_22
Snippet: A major issue with sigmoidal models is that they are often prone to overfitting [6] and also we in our preliminary experiments found such sigmoidal fits to be sensitive to initial conditions and to often require a large number of parameters. Previously, people have tried to experiment with regularizations [17] to account for such instabilities. Instead, we here choose to fit the logarithm of daily change of cases d, not the cumulative cases D. Th.....
Document: A major issue with sigmoidal models is that they are often prone to overfitting [6] and also we in our preliminary experiments found such sigmoidal fits to be sensitive to initial conditions and to often require a large number of parameters. Previously, people have tried to experiment with regularizations [17] to account for such instabilities. Instead, we here choose to fit the logarithm of daily change of cases d, not the cumulative cases D. The logarithm of d weights more evenly values close to the functions maximum and disregards other values. We believe this leads to a more stable model of d around its maximum, the turning point of D, and the time of interest since most relevant predictions such as peak of the pandemic, time point and width of peak are focused around this maximum.
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