GlaxoSmithKline US, Collegeville, PA.
Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, MA; Neuroradiology, Department of Radiology, Massachusetts General Hospital, Boston, MA.
Mayo Clin Proc. 2020 Sep;95(9):1898-1905. doi: 10.1016/j.mayocp.2020.06.027. Epub 2020 Jun 22.
To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) spread.
Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions - shelter-in-place and digital contact tracing - to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated.
Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelter-in-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated.
Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated.
建立并比较数字接触者追踪与就地避难对严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)传播的影响。
使用经典的流行病学框架和 2020 年 2 月 1 日至 2020 年 5 月 25 日期间发表的文献中估计的参数,我们对两种非药物干预措施(就地避难和数字接触者追踪)进行建模,以遏制 SARS-CoV-2 的传播。对于接触者追踪,我们假设采用一种先进的自动化接触者追踪(AACT)应用程序,根据个人的暴露情况向个人发送自我隔离的警报。模型参数包括下令就地避难的人口百分比、AACT 的采用率以及适当遵循建议的个人百分比。在这些变量的影响下,估计了感染、暴露和隔离的人数。
如果不采取任何干预措施,预计会出现感染率(>1000 万)高且早期峰值高的情况。就地避难以影响大量人群为代价,导致感染率迅速下降。AACT 模型实现了对感染和暴露人数的减少,与就地避难相似,而不会影响大量人群。例如,50%的 AACT 采用率模拟了 40%人口的就地避难令,可使感染峰值人数减少 90%以上。然而,与就地避难相比,采用 AACT 隔离的人数要少得多。
广泛采用数字接触者追踪可以像普遍的就地避难一样减轻感染的传播,但隔离的人数要少得多。