Author: Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann
Title: Inferring COVID-19 spreading rates and potential change points for case number forecasts Document date: 2020_4_6
ID: c8zfz8qt_66
Snippet: The remaining model parameters are constrained by uninformative priors, in practice the Half-Cauchy distribution [45] . The half-Cauchy distribution HalfCauchy(x, β) = 2/πβ[1 + (x/β) 2 ] is essentially a flat prior from zero to O(β) with heavy tails beyond. Thereby, β merely sets the order of magnitude that should not be exceeded for a given parameter. We chose for the number of initially infected people in the model (16 days before first d.....
Document: The remaining model parameters are constrained by uninformative priors, in practice the Half-Cauchy distribution [45] . The half-Cauchy distribution HalfCauchy(x, β) = 2/πβ[1 + (x/β) 2 ] is essentially a flat prior from zero to O(β) with heavy tails beyond. Thereby, β merely sets the order of magnitude that should not be exceeded for a given parameter. We chose for the number of initially infected people in the model (16 days before first data point) I 0 ∼ HalfCauchy(100) assuming an order of magnitude O(100) and below. In addition, we chose of the scale factor of the width of the likelihood function σ ∼ HalfCauchy(10), which is further multiplied to the number of new cases.
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