本文研究了德国Covid-19感染的扩散区域差异。机器学习选择过程用于减少来自影响变量池的变量。实证分析表明，各区域结构变量和区域聚合的人格特征对于不同的电晕传播非常重要。后一种特征表达联邦国家心态的差异。该国向东显示较低程度的受影响。高比例移民的地区显示出比其他人的发病率较高。如果忽视了个性特征，移民的影响被高估了。通过学校教育和贫困风险，确定了两种进一步的重要区域特征。联邦国家与低学校教育的人口不成比例地份额往往有更少的Covid-19案件。关于贫困，可以没有明确的陈述。 The more the population tends to be responsible towards fellow human beings, the higher is the risk of a more pronounced spread. Where there is a tendency towards altruism, which consists of helping other people, a higher level of COVID-19 infections is revealed. A significant positive correlation between infections and testing is shown by the estimates. The link between vaccinations and the number of infections is less clear. Across the three corona waves,significant changes emerge. This relates in particular to the proportion of migrants and the proportion of families at risk of poverty. The effects decrease over the course of the pandemic.