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

Our framework can be easily adapted to other countries and enables one to incorporate future developments. For other countries, or for forecasts within smaller communities (e.g. federal states or cities), additional details may become important, such as explicit modeling of incubation time distributions [17, 38] (i.e. as done in Fig. S4 ) , spatial heterogeneity [17, 21] , isolation effects [20, 38] , subsampling effects hiding undetected cases even beyond the reporting delay [39, 40] , or the age and contact structure of the population [26] . In countries where drastic changes in test coverage are expected this will have to be included as well. The methodology presented here is capable in principle of incorporating such details. It also lends itself to modeling of continuous drifts in the spreading rate, e.g. reflecting reactions of the public to news coverage of a catastrophic situation, or people growing tired of mitigation measures. Such further adaptations, however, can only be performed on a per-country basis by experts with an intimate knowledge of the local situation. Our code provides a solid and extensible base for this. For Germany, several developments in the near future may have to be included in the model. First, people may transiently change their behavior over the Easter holidays; second, we expect a series of change points, as well as continuous drifts, with governments trying to ease and calibrate mitigation measures.