Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?
复制标题

DOI:
10.1371/journal.pone.0259969
复制
发表时间:
2021
期刊:
影响因子:
3.7
通讯作者:
Farrahi K
Farrahi K
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Rusu AC;Emonet R;Farrahi K

文献摘要

参考文献

被引文献

相似文献

全面的检测计划,随后是充分的接触者追踪和隔离,是我们可以采用的最佳公共卫生干预措施,以减少在没有或有限的疫苗供应时持续流行病的影响,并避免全面封锁的影响。然而,对于SARS-CoV-2等高传染性病毒,追踪过程可能无效。基于访谈的方法往往会错过联系人并造成严重延迟,而数字解决方案可能会因采用率不足或使用模式不足而受到影响。在这里,我们提出了一种新的方法来模拟不同的接触追踪策略,使用广义多站点平均场模型,它可以自然地评估手动和数字方法的影响。我们的方法可以很容易地应用于任何房室配方,从而使更复杂的病原体动力学的研究。我们使用这种技术来模拟一个新定义的流行病学模型SEIR-T,并表明,在适当的条件下,即使数字化摄取不是最佳的,或者采访者错过了相当比例的接触,COVID-19流行病的追踪也是有效的。
Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.
DOI: 10.2196/18795
发表时间: 2020-04-01
影响因子: 8.5
作者:
Garg, Suneela;Bhatnagar, Nidhi;Gangadharan, Navya
通讯作者: Gangadharan, Navya
DOI: 10.1038/s41591-020-1092-0
发表时间: 2020-09-17
期刊: NATURE MEDICINE
影响因子: 82.9
作者:
Adam, Dillon C.;Wu, Peng;Cowling, Benjamin J.
通讯作者: Cowling, Benjamin J.
DOI: 10.1371/journal.pone.0095133
发表时间: 2014
期刊: PloS one
影响因子: 3.7
作者:
Farrahi K;Emonet R;Cebrian M
通讯作者: Cebrian M
DOI: 10.1126/science.286.5439.509
发表时间: 1999-10-15
期刊: SCIENCE
影响因子: 56.9
作者:
Barabási, AL;Albert, R
通讯作者: Albert, R
DOI: 10.1007/s11538-020-00726-x
发表时间: 2020-04-08
影响因子: 3.5
作者:
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者: Barrett, C. L.