Short-term modelling forecasts are vital for decision makers in the current pandemic. They require key epidemiological parameters and their evolution in time, and can be used to simulate effects of possible interventions and to estimate the actual effects of measures taken.
The model developed in this work has been used to quantify the effects of interventions in Germany during the month of March. It is shown that the first step (cancellation of large events) reduced the growth rate of infections from 30% to 12%. Closing of schools, childcare centers and a majority of stores brought a further reduction from 12% to 3%. Implementing the third step, a contact ban, caused to growth rate to become negative, implying a decrease of infections. However, the decay rate of about -3% implies that even a minor increase in spreading rate will again switch the dynamics to a regime with exponential growth.