论文标题
定量澄清有关COVID-19流行病学的关键问题
Quantitative clarification of key questions about COVID-19 epidemiology
论文作者
论文摘要
建模COVID-19的传播对于通知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力学的参数。 R_0，R_T，“串行间隔”和“生成间隔”等流行病学参数的含义可能具有挑战性，尤其是因为这些和其他参数在概念上是重叠的，有时是混乱的。此外，用于估算这些参数的过程做出了各种假设，并使用不同的数学方法，这些方法在依靠参数值并将其报告给公众时应理解和解释。在这里，我们提供了一些有关普遍报道的流行病学参数的推导的见解，并描述了预期锁定等缓解措施如何影响其价值。我们的目标是以最广泛的受众访问的方式介绍这些定量关系。我们希望更好地传达流行病学模型的复杂性，将改善我们对其优势和缺点的集体理解，并有助于避免使用时可能的陷阱。
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.
