Parag Kris V, Thompson Robin N, Donnelly Christl A
Department of Infectious Disease Epidemiology MRC Centre for Global Infectious Disease Analysis Imperial College London London UK.
Mathematics Institute University of Warwick Coventry UK.
J R Stat Soc Ser A Stat Soc. 2022 May 26. doi: 10.1111/rssa.12867.
statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, , is predominant among these statistics, measuring the average ability of an infection to multiply. However, encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, , that is, the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates of are more informative than those of . We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding public health interventions in real time.
统计数据通常源自疫情传播的简化模型，能实时为公共卫生政策提供信息。在这些统计数据中，瞬时再生数(R_t)最为突出，它衡量了感染传播的平均能力。然而，(R_t)没有编码时间信息，并且对建模假设敏感。因此，一些人提出了疫情增长率(r_t)，即对数变换后的病例发病率的变化率，作为一个在时间上更具意义且与模型无关的政策指导指标。我们检验这一观点，确定(r_t)的估计在何时以及是否比(R_t)的估计更具信息量。我们评估它们在了解病原体传播机制以及实时指导公共卫生干预方面的相对优势。
