Department of Ecology and Evolutionary Biology, Princeton, NJ, USA.
Department of Biology, McMaster University, Hamilton, Ontario, Canada.
J R Soc Interface. 2020 Jul;17(168):20200144. doi: 10.1098/rsif.2020.0144. Epub 2020 Jul 22.
A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of [Formula: see text] across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.
一种新型冠状病毒(SARS-CoV-2)于 2019 年 12 月成为全球威胁。随着疫情的发展,疾病建模者继续专注于估计基本繁殖数[Formula: see text] - 在易感人群中,由原发性病例引起的继发性病例的平均数量。尽管依赖于类似的数据源,但在疫情爆发初期,[Formula: see text]的建模方法和由此产生的估计值差异很大。在这里,我们通过将基本繁殖数分解为三个关键数量:指数增长率、平均世代间隔和世代间隔离散度,提出了一种用于比较和组合广泛模型中不同[Formula: see text]估计值的统计框架。我们将我们的框架应用于 SARS-CoV-2 爆发的早期[Formula: see text]估计值,表明许多[Formula: see text]估计值过于自信。我们的结果强调了在努力估计疫情爆发初期的[Formula: see text]时,在[Formula: see text]的所有组成部分中传播不确定性的重要性,包括世代间隔分布的形状。