In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19.
We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study.
The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.
在这项研究中,我们采用基于零截断似然的框架来估计k。我们通过使用随机模拟来评估估计性能,并将其与基线非截断版本进行比较。我们用COVID-19的三个接触者追踪数据集举例说明了分析框架。
我们证明了当出现0次病例的指标病例的欠确定时,估计偏差存在,零截断推断克服了这个问题,得到了k的偏差较小的估计。我们发现COVID-19的k推断为0.32(95CI:0.15,0.64),这似乎比许多以前的估计略小。我们在本研究中提供了应用推理框架的仿真代码。
对于偏差较小的传输异质性估计,建议使用零截断框架。这些发现强调了针对个体的病例管理策略的重要性,通过优先降低潜在超级传播者的传播风险来缓解COVID-19大流行。