发表机构
McMaster University; Sorbonne Université（麦克马斯特大学; 索邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用与疫情动量相关的守恒定律，从单个疫情时间序列中分离传播率与先验免疫力，分别推断\(R_0\)和\(x^-\)，通过随机模拟测试及对1918年流感大流行的重新评估得出相关数据。
AI 中文摘要
传染病时间序列常用于估计病原体的基本再生数\(R_0\)。然而，将流行模型拟合到时间序列会使病原体传播率与已有的群体免疫力混淆，所以只能推断出有效再生数\(R_{eff}\)。这个复合参数是潜在的\(R_0\)与流行前易感比例\(x^-\)的乘积。我们表明与疫情动量相关的守恒定律（感染潜力加权的患病率）使得从先验免疫力中分离传播率并从单个疫情时间序列中分别推断\(R_0\)和\(x^-\)成为可能。我们使用随机疫情模拟测试了该方法，并通过重新评估1918年大流行期间的流感传播率来说明该方法，估计而非假设群体先验免疫程度。对于美国费城的秋季疫情，我们发现\(R_0\approx2.7\)且\(x^-\approx0.8\)，这意味着在该波疫情之前约20%的人口已经免疫，可能是1918年春季先兆波感染的结果。
英文摘要
Infectious disease time series are often used to estimate a pathogen's basic reproduction number, $R_0$. However, fits of epidemic models to time series conflate pathogen transmissibility with pre-existing population immunity, so only the *effective* reproduction number, $R_{eff}$, can be inferred. This composite parameter is the product of the underlying $R_0$ and the pre-epidemic susceptible fraction, $x^-$. We show that a conservation law associated with epidemic momentum---prevalence weighted by potential to infect---makes it possible to disentangle transmissibility from prior immunity and to infer $R_0$ and $x^-$ separately from a single epidemic time series. We test the methodology using stochastic epidemic simulations, and illustrate the approach with a reappraisal of influenza transmissibility during the 1918 pandemic, estimating rather than assuming the degree of prior population immunity. For the autumn wave in Philadelphia, USA, we find $R_0\approx2.7$ and $x^-\approx0.8$, implying that about 20% of the population was already immune before that wave, plausibly as a result of infection during the spring 1918 herald wave.
Comments26 pages, 4 figures, 1 table. Generalized the method so estimates can be obtained before the epidemic reaches its peak; added a workflow figure and supporting appendices
