There is continued uncertainty in how long it takes a person infected by the COVID-19 virus to become infectious. In this paper, we quantify how this uncertainty affects estimates of the basic replication number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0, and thus estimates of the fraction of the population that would become infected in the absence of effective interventions. The analysis is general, and applies to all SEIR-based models, not only those associated with COVID-19. We find that when modeling a rapidly spreading epidemic, seemingly minor differences in how latency is treated can lead to vastly different estimates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0. We also derive a simple formula relating the replication number to the fraction of the population that is eventually infected. This formula is robust and applies to all compartmental models whose parameters do not depend on time.

中文翻译：

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延迟对 COVID-19 复制数估计的影响

感染 COVID-19 病毒的人需要多长时间才能具有传染性，仍然存在不确定性。在本文中，我们量化了这种不确定性如何影响基本复制数的估计 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \ usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0，从而估计在缺乏有效的干预措施。该分析是通用的，适用于所有基于 SEIR 的模型，而不仅仅是与 COVID-19 相关的模型。我们发现，在对迅速蔓延的流行病建模时，延迟处理方式上看似微小的差异可能会导致对 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage 的估计大不相同{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}R0。我们还推导出一个简单的公式，将复制数与最终被感染的人口比例联系起来。这个公式是稳健的，适用于所有参数不依赖于时间的分区模型。我们还推导出一个简单的公式，将复制数与最终被感染的人口比例联系起来。这个公式是稳健的，适用于所有参数不依赖于时间的分区模型。我们还推导出一个简单的公式，将复制数与最终被感染的人口比例联系起来。这个公式是稳健的，适用于所有参数不依赖于时间的分区模型。