The reproductive number $\mathcal{R}$ (or ${\mathcal{R}}_0$, the initial reproductive number in an immune-naïve population) has long been successfully used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some misconceptions about the predictive ability of the reproductive number, focusing on how it changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of $\mathcal{R}$ and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of $\mathcal{R}$ and ${\mathcal{R}}_0$ facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.

繁殖数$\mathcal{右}$（或者${\mathcal{右}}_0$，即未经免疫的人群中的初始繁殖数）长期以来一直被成功地用于预测病原体入侵的可能性，衡量流行病的潜在严重程度，并围绕干预措施制定政策。然而，经常被忽视的复杂性已经在指标的使用方面产生了混乱。这对于新出现的大流行病毒 SARS-CoV-2（COVID-19 的病原体）尤其明显。我们通过引用以下数学定义来解决关于再生数预测能力的一些误解，重点关注它如何随时间变化、随空间变化以及与流行病规模的关系$\mathcal{右}$以及当前大流行的例子。我们希望更好地理解其用途、细微差别和局限性$\mathcal{右}$和${\mathcal{右}}_0$有助于更好地了解 SARS-CoV-2 背景下的流行病传播、流行病严重程度以及干预措施的效果。