The Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Harvard College, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA.
Sci Rep. 2022 Feb 3;12(1):1857. doi: 10.1038/s41598-021-02605-4.
Amid COVID-19, many institutions deployed vast resources to test their members regularly for safe reopening. This self-focused approach, however, not only overlooks surrounding communities but also remains blind to community transmission that could breach the institution. To test the relative merits of a more altruistic strategy, we built an epidemiological model that assesses the differential impact on case counts when institutions instead allocate a proportion of their tests to members' close contacts in the larger community. We found that testing outside the institution benefits the institution in all plausible circumstances, with the optimal proportion of tests to use externally landing at 45% under baseline model parameters. Our results were robust to local prevalence, secondary attack rate, testing capacity, and contact reporting level, yielding a range of optimal community testing proportions from 18 to 58%. The model performed best under the assumption that community contacts are known to the institution; however, it still demonstrated a significant benefit even without complete knowledge of the contact network.
在 COVID-19 期间,许多机构投入了大量资源定期对其成员进行检测,以实现安全重启。然而,这种自我为中心的方法不仅忽略了周围的社区,也没有注意到可能突破机构防线的社区传播。为了测试一种更利他主义策略的相对优势,我们构建了一个流行病学模型,评估了当机构将其测试的一部分分配给其更大社区中成员的密切接触者时,对病例数的差异影响。我们发现,在所有合理的情况下,机构外部的测试对机构都有益,在基线模型参数下,最佳外部测试比例为 45%。我们的结果对当地流行率、二次攻击率、检测能力和接触报告水平具有稳健性,在最优社区检测比例的范围内为 18%至 58%。该模型在假设机构知道社区接触者的情况下表现最佳;然而,即使没有完整的接触网络知识,它仍然显示出显著的好处。