The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.
The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.
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DOI：
10.1038/s41467-021-21747-7
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发表时间：
2021-03-09
影响因子：
16.6
通讯作者：
Hens N
中科院分区：
文献类型：
--
作者：
Willem L;Abrams S;Libin PJK;Coletti P;Kuylen E;Petrof O;Møgelmose S;Wambua J;Herzog SA;Faes C;Beutels P;Hens N
COVID-19大流行导致许多政府实施限制社会交往的政策。有控制和持续地解除封锁措施涉及许多潜在的战略，需要进行广泛的情景分析。在这里，我们使用一个基于个人的模型（STRIDE）来模拟1100万比利时居民在不同层次上的互动，包括扩展的家庭环境，即“家庭泡沫”。COVID-19的负担受到身体接触的强度和频率的影响，因此，家庭泡沫有可能减少90%的住院率。此外，我们发现在症状出现4天后完成接触者追踪至关重要。对儿童易感性的假设会影响学校复课的影响，尽管我们发现与商业和休闲相关的社会混合模式对COVID-19相关疾病负担的影响更大。研究中的缓解政策的最佳部署需要及时遵守物理距离、检测和自我隔离。COVID-19大流行导致许多政府实施限制社会交往的政策。在这里，作者实现了一个年龄特定的，以个人为基础的模型与数据的社会接触比利时人口和调查非药物干预的影响。
The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., “household bubbles”. The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation. The COVID-19 pandemic caused many governments to impose policies restricting social interactions. Here, the authors implement an age-specific, individual-based model with data on social contacts for the Belgian population and investigate the effect of non-pharmaceutical interventions.
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影响因子：
5
作者：
Wallinga, Jacco;Teunis, Peter;Kretzschmar, Mirjam
通讯作者：
Kretzschmar, Mirjam
影响因子：
3.5
作者：
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者：
Barrett, C. L.
传染病传播的行为变化模型：系统评价（2010-2015）。
DOI：
10.1098/rsif.2016.0820
发表时间：
2016-12
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Verelst F;Willem L;Beutels P
通讯作者：
Beutels P
影响因子：
4.6
作者：
Kuylen E;Willem L;Broeckhove J;Beutels P;Hens N
通讯作者：
Hens N
影响因子：
9.3
作者：
Torneri, Andrea;Libin, Pieter;Hens, Niel
通讯作者：
Hens, Niel