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_48
Snippet: In conclusion, the presented Bayesian approach allows to detect and quantify the effect of recent governmental interventions and -combined with potential subsequent interventions -to forecast future case number scenarios. Our analysis highlights the importance of precise timing and magnitude of interventions for future case numbers. It also stresses the importance of including the reporting delay D between the date of infection and the date of th.....
Document: In conclusion, the presented Bayesian approach allows to detect and quantify the effect of recent governmental interventions and -combined with potential subsequent interventions -to forecast future case number scenarios. Our analysis highlights the importance of precise timing and magnitude of interventions for future case numbers. It also stresses the importance of including the reporting delay D between the date of infection and the date of the confirmed case in the model . The delay D, together with the time required to implement interventions causes a total delay between an intervention and its visibility in the case numbers of about two weeks for COVID-19 in Germany. This means that changes in our behavior today can only be detected in confirmed cases in two weeks. Combined with the current spreading rate that is still around zero, the inferred spreading and observation dynamics warrant an extremely careful planning of future measures.
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