Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
The Tokyo Foundation for Policy Research, Tokyo, Japan.
BMC Infect Dis. 2022 Jul 28;22(1):656. doi: 10.1186/s12879-022-07646-2.
Multiple waves of the COVID-19 epidemic have hit most countries by the end of 2021. Most of those waves are caused by emergence and importation of new variants. To prevent importation of new variants, combination of border control and contact tracing is essential. However, the timing of infection inferred by interview is influenced by recall bias and hinders the contact tracing process.
We propose a novel approach to infer the timing of infection, by employing a within-host model to capture viral load dynamics after the onset of symptoms. We applied this approach to ascertain secondary transmission which can trigger outbreaks. As a demonstration, the 12 initial reported cases in Singapore, which were considered as imported because of their recent travel history to Wuhan, were analyzed to assess whether they are truly imported.
Our approach suggested that 6 cases were infected prior to the arrival in Singapore, whereas other 6 cases might have been secondary local infection. Three among the 6 potential secondary transmission cases revealed that they had contact history to previously confirmed cases.
Contact trace combined with our approach using viral load data could be the key to mitigate the risk of importation of new variants by identifying cases as early as possible and inferring the timing of infection with high accuracy.
截至 2021 年底，大多数国家都经历了多波 COVID-19 疫情。这些疫情大多是由新变种的出现和输入引起的。为了防止新变种的输入，结合边境管控和接触者追踪至关重要。然而，通过访谈推断的感染时间会受到回忆偏差的影响，从而阻碍接触者追踪过程。
我们提出了一种新的方法来推断感染时间，通过使用宿主内模型来捕获症状出现后的病毒载量动态。我们应用这种方法来确定可能引发疫情的二次传播。作为一个演示，对新加坡最初报告的 12 例病例进行了分析，这些病例被认为是由于近期前往武汉的旅行史而被视为输入性病例，以评估它们是否真的是输入性病例。
我们的方法表明，6 例病例在抵达新加坡之前就已感染，而其他 6 例病例可能是本地二次感染。在 6 例潜在的二次传播病例中，有 3 例显示与先前确诊病例有接触史。
结合接触者追踪和我们使用病毒载量数据的方法，可以通过尽早识别病例并准确推断感染时间，成为减轻新变种输入风险的关键。
