Household bubbles and COVID-19 transmission: insights from percolation theory.
Household bubbles and COVID-19 transmission: insights from percolation theory.
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DOI：
10.1098/rstb.2020.0284
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发表时间：
2021-07-19
期刊：
影响因子：
--
通讯作者：
Brooks-Pollock E
中科院分区：
文献类型：
--
作者：
Danon L;Lacasa L;Brooks-Pollock E
在为遏制 COVID-19 传播而采取社交距离措施的时代，冒泡是将两个或更多家庭合并起来，创建一个专属的更大群体。由于涉及复杂的社会结构，冒泡对 COVID-19 传播的影响很难量化。我们开发了英国家庭的网络描述，使用配置模型来链接家庭。我们通过将不同规模的家庭联合起来，探讨了冒泡情景的影响。对于每个冒泡场景，我们从数值和理论上计算了渗透阈值，即形成巨型组件所需的每个个体的连接数。我们将渗透阈值与家庭再生数联系起来。我们发现单人家庭与另一个家庭加入的冒泡场景对网络连接和传输潜力的影响最小。所有家庭形成泡沫的普遍情况很可能导致难以控制的大范围传播。通过减少家庭以外的接触数量，可以减轻具有可变吸收和异质泡沫大小的合理情景的影响。家庭隔离会增加传播风险；然而，在某些情况下，风险可能很小，可以通过其他行为改变来平衡。本文是主题“塑造英国早期 COVID-19 大流行应对措施的建模”的一部分。
In the era of social distancing to curb the spread of COVID-19, bubbling is the combining of two or more households to create an exclusive larger group. The impact of bubbling on COVID-19 transmission is challenging to quantify because of the complex social structures involved. We developed a network description of households in the UK, using the configuration model to link households. We explored the impact of bubbling scenarios by joining together households of various sizes. For each bubbling scenario, we calculated the percolation threshold, that is, the number of connections per individual required for a giant component to form, numerically and theoretically. We related the percolation threshold to the household reproduction number. We find that bubbling scenarios in which single-person households join with another household have a minimal impact on network connectivity and transmission potential. Ubiquitous scenarios where all households form a bubble are likely to lead to an extensive transmission that is hard to control. The impact of plausible scenarios, with variable uptake and heterogeneous bubble sizes, can be mitigated with reduced numbers of contacts outside the household. Bubbling of households comes at an increased risk of transmission; however, under certain circumstances risks can be modest and could be balanced by other changes in behaviours. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK'.
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影响因子：
44.1
作者：
Pastor-Satorras, Romualdo;Castellano, Claudio;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
4.3
作者：
Ball, Frank;Sirl, David;Trapman, Pieter
通讯作者：
Trapman, Pieter
影响因子：
4.3
作者：
Pellis, Lorenzo;Ball, Frank;Trapman, Pieter
通讯作者：
Trapman, Pieter
影响因子：
--
作者：
Leng, Trystan;White, Connor;Flasche, Stefan
通讯作者：
Flasche, Stefan
影响因子：
4.3
作者：
Goldstein, E.;Paur, K.;Lipsitch, M.
通讯作者：
Lipsitch, M.