论文标题
对COVID-19的发病率数据进行建模见解：第二部分 - 为什么隔间模型如此准确？
Modeling Insights from COVID-19 Incidence Data: Part II -- Why are compartment models so accurate?
论文作者
论文摘要
SIR室模型是描述疾病通过人群传播的最简单模型之一。该模型使一个不切实际的假设是，疾病传播的种群被充分混合。尽管实际人群在SIR模型中未代表的联系中具有异质性，但是在整个大流行期间，它仍然非常适合美国国家数据。在这里，我们在数学上证明了简单连续的SIR模型如何近似于一个模型，该模型包括异质触点，并提供有关如何解释在异质动力学背景下从回归中收集的参数的见解。
The SIR-compartment model is among the simplest models that describe the spread of a disease through a population. The model makes the unrealistic assumption that the population through which the disease is spreading is well-mixed. Although real populations have heterogeneities in contacts not represented in the SIR model, it nevertheless well fits real U.S. state data at multiple points throughout the pandemic. Here we demonstrate mathematically how closely the simple continuous SIR model approximates a model which includes heterogeneous contacts, and provide insight onto how one can interpret parameters gleaned from regression in the context of heterogeneous dynamics.
