From the WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom.
Epidemiology. 2023 Mar 1;34(2):201-205. doi: 10.1097/EDE.0000000000001563. Epub 2022 Dec 13.
The time-varying reproduction number, Rt, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating Rt seldom account for the impact of overdispersion on transmission.
We developed a negative binomial framework to estimate Rt and a time-varying dispersion parameter (kt). We applied the framework to COVID-19 incidence data in Hong Kong in 2020 and 2021. We conducted a simulation study to compare the performance of our model with the conventional Poisson-based approach.
Our framework estimated an Rt peaking around 4 (95% credible interval = 3.13, 4.30), similar to that from the Poisson approach but with a better model fit. Our approach further estimated kt <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that kt decreased sharply to around 0.4 when a large cluster of infections occurred.
Our proposed approach can contribute to the estimation of Rt and monitoring of the time-varying dispersion parameters to quantify the role of superspreading.
时变繁殖数 Rt 常用于监测传染病在流行期间的传播能力，但估计 Rt 的标准方法很少考虑过离散对传播的影响。
我们开发了一个负二项式框架来估计 Rt 和时变离散参数（kt）。我们将该框架应用于 2020 年和 2021 年香港的 COVID-19 发病率数据。我们进行了一项模拟研究，以比较我们的模型与传统泊松基础方法的性能。
我们的框架估计 Rt 在 4 左右达到峰值（95%可信区间=3.13，4.30），与泊松方法相似，但拟合效果更好。我们的方法进一步估计在两波疫情开始时 kt<0.5，表明传播存在明显的异质性。我们还发现，当发生大规模感染集群时，kt 急剧下降至约 0.4。
我们提出的方法可以有助于估计 Rt 和监测时变离散参数，以量化超级传播者的作用。
