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摘要翻译：
对新冠肺炎病毒的传播进行建模对于制定公共卫生政策至关重要。新冠肺炎流行病学的所有模型都依赖于描述感染过程动力学的参数。流行病学参数如R_0、R_t、“序列间隔”和“世代间隔”的含义可能难以理解，尤其是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做出了各种假设，并使用了不同的数学方法，在依赖参数值并向公众报告时，这些方法应该得到理解和说明。在这里，我们提供了几个关于常见报告的流行病学参数的推导的见解，并描述了像封锁这样的缓解措施预计将如何影响它们的值。我们的目标是以一种最广泛的受众可以获得的方式呈现这些数量关系。我们希望，更好地交流流行病学模型的复杂性将提高我们对其优缺点的集体理解，并将有助于避免在使用它们时可能出现的陷阱。
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英文标题：
《Quantitative clarification of key questions about COVID-19 epidemiology》
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作者：
Yinon M. Bar-On, Ron Sender, Avi I. Flamholz, Rob Phillips, Ron Milo
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最新提交年份：
2020
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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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.
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PDF链接：
https://arxiv.org/pdf/2007.05362
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