Ferrari Alberto, Santus Enrico, Cirillo Davide, Ponce-de-Leon Miguel, Marino Nicola, Ferretti Maria Teresa, Santuccione Chadha Antonella, Mavridis Nikolaos, Valencia Alfonso
FROM Research Foundation, Papa Giovanni XXIII Hospital, Bergamo, Italy.
Bayer, Decision Science & Advanced Analytics for MA, PV & RA Division, Leverkusen, Germany.
NPJ Digit Med. 2021 Jan 14;4(1):9. doi: 10.1038/s41746-020-00374-4.
Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.
通过手机应用程序进行有针对性的接触者追踪，已被提议作为一种手段，以帮助遏制新冠病毒的传播，并管理美国和欧洲目前正在实施的全国范围封锁措施的解除。然而，对于其潜在效果仍存在争议，尤其是考虑到不同地区的人口统计学特征。我们构建了一个扩展的新冠疫情SIR模型，该模型考虑了特定地区的人口密度，并利用它来测试接触者追踪应用程序在多种情景下的影响。利用来自意大利和西班牙的人口统计和流动性数据，我们使用该模型模拟了在基线接触率、人口密度和应用程序用户在总人口中所占比例方面各不相同的情景。我们的结果表明，为支持对有症状病例的有效隔离，即使应用程序用户比例相对较小，应用程序介导的接触者追踪也能成功缓解疫情，而用户比例较大时甚至能完全抑制疫情。然而，当考虑到人口密度的地区差异时，在高密度地区控制疫情可能会显著更加困难，这凸显了这种干预措施在特定背景下的潜在局限性。这项工作证实了先前支持应用程序介导的接触者追踪作为新冠疫情缓解措施的结果，并提请注意特定地区的人口统计和流动性因素对于在遏制政策中实现最大效果的重要性。
