The COVID-19 pandemic has led to numerous mathematical models for the spread of infection, the majority of which are large compartmental models that implicitly constrain the generation-time distribution. On the other hand, the continuous-time Kermack–McKendrick epidemic model of 1927 (KM27) allows an arbitrary generation-time distribution, but it suffers from the drawback that its numerical implementation is rather cumbersome. Here, we introduce a discrete-time version of KM27 that is as general and flexible, and yet is very easy to implement computationally. Thus, it promises to become a very powerful tool for exploring control scenarios for specific infectious diseases such as COVID-19. To demonstrate this potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and the same initial growth rate, compartmental models systematically predict lower peak sizes than models in which the latent and the infectious period have fixed duration.

COVID-19 大流行导致了许多用于感染传播的数学模型，其中大多数是隐含地限制生成时间分布的大型隔室模型。另一方面，1927 年的连续时间 Kermack-McKendrick 流行病模型 (KM27) 允许任意生成时间分布，但它的缺点是其数值实现相当麻烦。在这里，我们介绍了 KM27 的离散时间版本，它具有通用性和灵活性，但在计算上非常容易实现。因此，它有望成为探索特定传染病（例如 COVID-19）控制方案的非常强大的工具。为了证明这种潜力，我们从数值上研究了发病峰大小如何取决于模型成分。我们发现，