背景：COVID-19在香港的“第三波”，中国受到非药物干预（NPI）的压制。尽管社会距离条例很快得到加强，疫情持续增长，导致跟踪和测试的延迟增加。出台了进一步的规定，加上针对风险群体的“目标测试”服务。估计单个NPI的影响可以提供有关如何在没有彻底封锁的情况下控制疫情的经验教训。然而,确认时间的不断变化的延迟对当前的建模方法提出了挑战。我们使用了一种新的方法，旨在解开和量化个人干预的效果。
方法：我们将跟踪和测试中延迟的原因（即负载-效率关系）以及此类延迟的后果（即未跟踪案例的比例和具有确认延迟的跟踪案例的比例）纳入确定性传输模型，适用于每天有和没有epi-link的病例数（表明已进行了联系追踪）。然后计算每个NPI的效果。
结果：该模型估计，在较早放松法规之后，Re从0.7升至3.2。由于联系人追踪系统的负载导致确认延迟增加，因此恢复社交距离仅将Re降低到1.3。然而,Re在引入针对性测试后减少了20.3%，在扩展面罩规则后减少了17.5%，将Re降至0.9，抑制疫情。没有结合延迟的模型的输出未能捕获传输和Re的重要特征。
结论：改变确认延迟对疾病传播和传播性的估计有显著影响。这导致了一个明确的建议，即应在爆发期间监控和缓解延迟，延迟动力学应纳入模型以评估NPI的影响。
背景：香港城市大学和卫生医学研究基金。
BACKGROUND: The \'third wave\' of COVID-19 in Hong Kong,
China was suppressed by non-pharmaceutical interventions (NPIs). Although social distancing regulations were quickly strengthened, the outbreak continued to grow, causing increasing delays in tracing and testing. Further regulations were introduced, plus \'targeted testing\' services for at-risk groups. Estimating the impact of individual NPIs could provide lessons about how outbreaks can be controlled without radical lockdown. However, the changing delays in confirmation time challenge current modelling methods. We used a novel approach aimed at disentangling and quantifying the effects of individual interventions.
METHODS: We incorporated the causes of delays in tracing and testing (i.e. load-efficiency relationship) and the consequences from such delays (i.e. the proportion of un-traced cases and the proportion of traced-cases with confirmation delay) into a deterministic transmission model, which was fitted to the daily number of cases with and without an epi‑link (an indication of being contact-traced). The effect of each NPI was then calculated.
RESULTS: The model estimated that after earlier relaxation of regulations, Re rose from 0.7 to 3.2. Restoration of social distancing to the previous state only reduced Re to 1.3, because of increased delay in confirmation caused by load on the contact-tracing system. However, Re decreased by 20.3% after the introduction of targeted testing and by 17.5% after extension of face-mask rules, reducing Re to 0.9 and suppressing the outbreak. The output of the model without incorporation of delay failed to capture important features of transmission and Re.
CONCLUSIONS: Changing delay in confirmation has a significant impact on disease transmission and estimation of transmissibility. This leads to a clear recommendation that delay should be monitored and mitigated during outbreaks, and that delay dynamics should be incorporated into models to assess the effects of NPIs.
BACKGROUND: City University of Hong Kong and Health and Medical Research Fund.
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