The paper by Hufnagel, Brockmann, and Geisel introduces a probabilistic model to forecast the global spread of infectious diseases, particularly focusing on the SARS outbreak. The model combines stochastic local infection dynamics with stochastic transport on a global network, incorporating civil aviation traffic. The authors demonstrate that the model can predict the geographical spread of epidemics with surprising accuracy, aligning well with published case reports. They attribute this high predictability to the strong heterogeneity of the network. The model is used to identify endangered regions and analyze the effectiveness of different control strategies, emphasizing the importance of a quick and focused response to inhibit the global spread of epidemics. The study highlights the need for detailed analysis of the aviation network to develop efficient quarantine strategies in a globalized world.The paper by Hufnagel, Brockmann, and Geisel introduces a probabilistic model to forecast the global spread of infectious diseases, particularly focusing on the SARS outbreak. The model combines stochastic local infection dynamics with stochastic transport on a global network, incorporating civil aviation traffic. The authors demonstrate that the model can predict the geographical spread of epidemics with surprising accuracy, aligning well with published case reports. They attribute this high predictability to the strong heterogeneity of the network. The model is used to identify endangered regions and analyze the effectiveness of different control strategies, emphasizing the importance of a quick and focused response to inhibit the global spread of epidemics. The study highlights the need for detailed analysis of the aviation network to develop efficient quarantine strategies in a globalized world.