COVID–19 continues to disrupt social lives and the economy of many countries and challenges their healthcare capacities. In Germany, the number of cases increased exponentially in early March 2020. As a political reaction, social restrictions were imposed by closing e.g. schools, shops, cafés and restaurants, as well as borders for travellers. This reaped success as the infection rate descended significantly in early April. In mid July, however, the numbers started to rise again. Of particular reasons was that from 15 June onwards, the travel ban has widely been cancelled or at least loosened. Here, we present an extended susceptible–exposed–infected–recovered–deceased (SEIRD) model to describe the disease dynamics in Germany, taking into account German travellers which returned infected from abroad. Epidemiological parameters like transmission rate, lethality or detection rate of infected individuals, as well as a rate measuring the impact of these travellers, were estimated by fitting the model output to available data. Parameter estimation was performed via Bayesian inference with the aid of the Monte–Carlo–based Metropolis algorithm. We found that travellers had a strong impact on the overall infection cases. Until the end of August, roughly 50,000 cases directly or indirectly related to travellers were estimated. These obviously caused even higher infection cases later on, which among other causes lead to a second wave of infection cases in late 2020. We conclude that travel restrictions are an important tool for controlling infection cases during pandemics which can still have an impact on the upcoming summer in case the currently high vaccination rates can not prevent further infection waves.