Mathematical Institute, University of Oxford, Oxford, United Kingdom.
Mathematics Institute, University of Warwick, Coventry, United Kingdom.
Elife. 2021 Apr 26;10:e65534. doi: 10.7554/eLife.65534.
Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing.
Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches.
The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms.
High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced.
Engineering and Physical Sciences Research Council (EPSRC).
了解 SARS-CoV-2 感染期间的传染性变化对于评估接触者追踪等公共卫生措施的效果至关重要。
在这里，我们开发了一种新的机制方法，使用已知的感染者-被感染者对的数据来推断 SARS-CoV-2 感染者的传染性特征。我们将使用我们的机制方法生成的关键流行病学数量的估计与使用先前方法生成的类似估计进行了比较。
与常用方法相比，机制方法对 SARS-CoV-2 感染者-被感染者数据的拟合度更好。我们的最佳拟合模型表明，有很大一部分是在出现症状前就已经具有传染性，并且许多传播发生在感染者出现症状前不久。
在症状出现前就具有高传染性，强调了在广泛分发有效疫苗之前，继续进行接触者追踪的重要性，即使仅追踪症状出现前短时间窗口内的接触者。
工程和物理科学研究理事会（EPSRC）。
