关于COVID-19流行病学关键问题的定量澄清
Quantitative clarification of key questions about COVID-19 epidemiology
摘要：
建立COVID-19传播模型对于制定公共卫生政策至关重要。COVID-19流行病学的所有模型都依赖于描述感染过程动态的参数。流行病学参数如R_0、R_t、“连续间隔”和“世代间隔”的含义可能难以理解，特别是因为这些参数和其他参数在概念上重叠，有时命名混乱。此外，用于估计这些参数的程序作出了各种假设，并使用了不同的数学方法，在依赖参数值并向公众报告这些参数值时，应该理解和说明这些假设和方法。在这里，我们提供了一些关于推导常见的流行病学参数的见解，并描述了如何缓解措施，如锁定预计将影响其值。我们的目标是以尽可能广泛的受众可以访问的方式呈现这些定量关系。我们希望，更好地传达流行病学模型的复杂性将提高我们对它们的优点和缺点的集体理解，并将有助于避免在使用它们时可能出现的陷阱。
英文摘要：
Modeling the spread of COVID-19 is crucial for informing public health policy. All models for COVID-19 epidemiology rely on parameters describing the dynamics of the infection process. The meanings of epidemiological parameters like R_0, R_t, the "serial interval" and "generation interval" can be challenging to understand, especially as these and other parameters are conceptually overlapping and sometimes confusingly named. Moreover, the procedures used to estimate these parameters make various assumptions and use different mathematical approaches that should be understood and accounted for when relying on parameter values and reporting them to the public. Here, we offer several insights regarding the derivation of commonly-reported epidemiological parameters, and describe how mitigation measures like lockdown are expected to affect their values. We aim to present these quantitative relationships in a manner that is accessible to the widest audience possible. We hope that better communicating the intricacies of epidemiological models will improve our collective understanding of their strengths and weaknesses, and will help avoid possible pitfalls when using them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
