Big Data Institute, University of Oxford, Oxford, UK.
MathSys CDT, University of Warwick, Coventry, UK.
Nat Commun. 2021 Sep 13;12(1):5412. doi: 10.1038/s41467-021-25531-5.
Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R. We reaffirm that contact tracing is not currently appropriate as the sole control measure.
新出现的证据表明，接触者追踪在英国降低 COVID-19 大流行期间的 R 数方面收效有限。我们通过扩展现有的分支过程接触者追踪模型，增加诊断性检测并改进参数估计，来研究潜在的陷阱和改进领域。我们的研究结果表明，报告和遵守是计划影响的最重要预测因素，但追踪覆盖范围和速度以及诊断敏感性也起着重要作用。我们的结论是，实施良好的接触者追踪可以为控制和预防疫情带来微小但潜在的好处，将 R 数降低 15%。我们重申，接触者追踪目前不适合作为唯一的控制措施。
