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Abstract：
Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors. In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied. The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.
摘要：
自2020年春季COVID-19大流行爆发以来，测试的概念，trace,和分离物（TTI）被用作非药物干预措施，以防止疾病的进一步传播。特此,最近新确诊的SARS-CoV-2感染者的接触伙伴与最初发现的病例一起被确定和隔离，以避免潜在的继发感染。虽然政策是，鉴于被追踪人员的遵守，通常被认为是有效的，关于网络特定的影响因素知之甚少。在这项工作中,我们旨在评估TTI策略在(1)用于不同传染性水平的疾病和(2)用于不同接触网络时的有效性.对于先前的，我们改变每次接触的感染概率，对于后者，我们分析了不同的聚类系数。我们的目标是检验两个假设的有效性：首先，如果疾病的传染性很小，我们预计该政策会更有效，因为隔离人员的时间延迟至关重要。第二,由于友谊悖论的影响，如果基础联系网络的聚类系数较高，我们预计该政策将更有效。我们利用了基于代理的网络模型，该模型由三个交织在一起的模型部分组成：流行病学SEIR模型，隔离模型和联系人跟踪模型。为了测试假设，基础接触网络的疾病参数和聚类系数是变化的。仿真结果表明,的确,跟踪似乎对具有较高聚类的网络有更大的遏制影响，特别是对于快速传播的疾病。然而，与疾病传染性的影响相比，影响很小。因此,我们发现，传播概率越高，政策有效性就会显著下降。后者意味着跟踪和隔离接触的遏制影响变得更加有效，如果有限制感染概率的其他措施支持，或者如果在负面季节性影响较低的时期应用。
