回到根源：适应新冠肺炎的离散Kermack-McKendrick模型
Back to the roots: A discrete Kermack-McKendrick model adapted to Covid-19
摘要：
分析2019冠状病毒病大流行的一个广泛使用的工具是标准SIR模型。它似乎经常被用作黑箱，而没有考虑到这个模型是作为1927年开创性的Kermack-McKendrick理论的特殊情况导出的。这是我们的出发点。我们解释了设置的Kermack-McKendrick理论（传递到一个离散的方法），并使用医疗信息专门的模型，我们称之为{ \em适应K-McK-model}。这包括疫苗接种、大规模测试和突变体的影响。我们证明了该模型的使用，将其应用到德国的发展。作为一个引人注目的应用程序，我们证明了一个相对温和的干预减少隔离的时间由一天导致了显着的改善。正如我们将证明的那样，通过某些质量测试可以获得类似的效果。我们讨论的可能性，应用模型的预测和分析工具。我们比较了适应K-McK-model的标准SIR模型，并观察condiderable差异，如果接触率是不恒定的。最后，我们比较了罗伯特科赫研究所确定的经验再生产率的模型。
英文摘要：
A widely used tool for analysing the Covid-19 pandemic is the standard SIR model. It seems often to be used as a black box, not taking into account that this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927. This is our starting point. We explain the setup of the Kermack-McKendrick theory (passing to a discrete approach) and use medical information for specializing to a model which we call {\em adapted K-McK-model}. This includes effects of vaccination, mass testing and mutants. We demonstrate the use of the model by applying it to the development in Germany. As a striking application we demonstrate that a comparatively mild intervention reducing the time until quarantine by one day leads to a drastic improvement. A similar effect can be obtained by certain mass testings as we will demonstrate. We discuss possibilities to apply the model both for predictions and as an analysis tool. We compare the adapted K-McK-model to the standard SIR model and observe condiderable differences if the contact rates are not constant. Finally we compare the model reproduction rate with the empirical reproduction rate determined by the Robert Koch-Institut.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
