The basic reproductive number, R0, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R0. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R0 and the underlying heterogeneity. Importantly, epidemics with lower R0 can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R0, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R0.

中文翻译：

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超越 R 0 ：继发感染的异质性和概率流行病预测

基本再生数 R0 是公共卫生领域最常见和最常被误用的数字之一。这个单一数字通常用于比较疫情暴发和预测大流行风险，但它掩盖了不同流行病可能表现出的复杂性，即使它们具有相同的 R0。在这里，我们重新表述并扩展了随机网络理论的经典结果，利用其平均 R0 和潜在的异质性，利用对继发感染分布的估计来预测流行病的规模。重要的是，如果 R0 较低的流行病传播更加均匀（因此对随机波动的抵抗力更强），那么它们的规模可能会更大。我们在没有重大干预的情况下使用不同的真实流行病以及已知的 R0、异质性和流行病规模估计值来说明这种方法的潜力。此外，我们讨论了在新兴病原体数据稀缺的现实中实施该框架的不同方式。最后，我们证明，如果没有关于新出现的传染病（如 COVID-19）继发感染异质性的数据，疫情规模的不确定性范围会很大。总而言之，我们的工作强调了在新发传染病爆发期间进行接触者追踪的迫切需要以及超越 R0 的需要。