Inferring COVID-19 spreading rates and potential change points for case number forecasts

Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann

We presented a Bayesian approach for online-monitoring the effect of governmental interventions on the spread of an epidemic outbreak. This approach enabled a timely inference of the central epidemiological parameters for Germanyas well as three change points in the spreading rate -of an SIR model from the number of reported cases during the COVID-19 outbreak. We showed that change points in the spreading rate affect the confirmed case numbers with a delay of about two weeks (median reporting delay of D = 9.3 days plus a median duration of changes of 3 days). Thereby, we were able to relate the inferred change points to the three major governmental interventions in Germany: We found a clear reduction of the spreading rate related to each governmental intervention (Fig. 3) , (i) the cancellation of large events with more than 1000 participants (around March 9), (ii) the closing of schools, childcare centers and the majority of stores (in effect March 16), and (iii) the contact ban and closing of all non-essential stores (in effect March 23).