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Abstract：
In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates.
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.
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.
摘要：
在传染病传播动力学中，个体传染性的高度异质性表明，很少有指标病例产生大量的二次病例，这通常被称为超级传播事件。传输中的异质性可以通过将二次案例的数量分布描述为具有色散参数的负二项（NB）分布来衡量，k.However,这样的推理框架通常忽略了对零星案例的欠确定，它们是那些没有已知流行病学联系的人，被认为是独立的一类，这可能会使估计产生偏差。
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。
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。
