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 R0—the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of R0 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 R0 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 R0 for the SARS-CoV-2 outbreak, showing that many R0 estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of R0, including the shape of the generation-interval distribution, in efforts to estimate R0 at the outset of an epidemic.

中文翻译：

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协调基本再生数的早期爆发估计及其不确定性：新型冠状病毒（SARS-CoV-2）爆发的框架和应用

一种新型冠状病毒 (SARS-CoV-2) 于 2019 年 12 月成为一种全球威胁。随着流行病的发展，疾病建模者继续专注于估计基本再生数 R0——由原发病例引起的继发病例平均数。否则易感人群。尽管依赖类似的数据源，但在疫情爆发初期，建模方法和由此得出的 R0 估计值差异很大。在这里，我们提出了一个统计框架，通过将基本再生数分解为三个关键量：指数增长率、平均世代间隔和世代间隔离散度，比较和组合各种模型中不同的 R0 估计值。我们将我们的框架应用于 SARS-CoV-2 爆发的 R0 的早期估计，表明许多 R0 估计过于自信。我们的结果强调了在 R0 的所有组成部分中传播不确定性的重要性，包括生成间隔分布的形状，以便在流行病开始时估计 R0。