Chopping the tail: How preventing superspreading can help to maintain COVID-19 control.
Chopping the tail: How preventing superspreading can help to maintain COVID-19 control.
复制标题
切断尾巴：如何防止超级传播有助于维持COVID-19控制。
DOI：
10.1016/j.epidem.2020.100430
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发表时间：
2021-03
期刊：
影响因子：
3.8
通讯作者：
Mordecai EA
中科院分区：
文献类型：
--
作者：
Kain MP;Childs ML;Becker AD;Mordecai EA
疾病传播是众所周知的异质性，SARS-CoV-2也不例外。在偏态分布中，少数个人或事件对大部分传播负有责任，这可能导致爆炸性的超级传播事件，从而产生快速和波动的流行动态，特别是在流行病的早期或晚期。预测和防止超级传播事件可以大大降低总体传输速率。在这里，我们提出了一个随机房室（SEIR）流行病学模型框架，用于从多个不完全观察到的数据流，包括报告的病例，死亡和基于移动的手机的流动性，采用先前的估计SARS-CoV-1和SARS-CoV-2的传输中的个人水平的异质性估计传输参数。我们通过估计随时间变化的传播率来参数化COVID-19流行动力学模型，该传播率纳入了随着时间变化的非药物干预策略的影响，在五个流行病学上不同的环境中-加州的洛杉矶县和圣克拉拉县;华盛顿的西雅图（金郡）;格鲁吉亚的亚特兰大（迪卡尔布县和富尔顿县）;以及佛罗里达的迈阿密（迈阿密-戴德县）。我们发现，在2020年3月中旬发布社交距离命令后，所有五个地点的有效繁殖数（）迅速降至1以下，但从4月中旬开始逐渐增加的流动性导致5月下旬（洛杉矶，迈阿密和亚特兰大）或6月上旬（圣克拉拉县和西雅图）再次高于1。然而，我们发现，从7月中旬开始为应对疫情复苏而增加的社交距离在8月14日再次降至1以下。接下来，我们使用拟合的模型来问：截断个体水平的传播率分布（它删除了个体传播率特别高的时间段，从而对超级传播事件进行建模）如何影响流行病动态和控制？我们发现，截断传播率分布同时部分放松社交距离的干预措施广泛有效，对疫情增长的影响与2020年4月观察到的最强人群社交距离相当。鉴于在疫苗广泛使用之前，需要采取社交距离干预措施来维持流行病控制，“切断尾巴”以降低超级传播事件的概率是一种有希望的选择，可以缓解对极端普遍社交距离的需求。
Disease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a stochastic compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find that the effective reproduction number () dropped below 1 rapidly in all five locations following social distancing orders in mid-March, 2020, but that gradually increasing mobility starting around mid-April led to an once again above 1 in late May (Los Angeles, Miami, and Atlanta) or early June (Santa Clara County and Seattle). However, we find that increased social distancing starting in mid-July in response to epidemic resurgence once again dropped below 1 in all locations by August 14. We next used the fitted model to ask: how does truncating the individual-level transmission rate distribution (which removes periods of time with especially high individual transmission rates and thus models superspreading events) affect epidemic dynamics and control? We find that interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing.
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DOI：
10.1098/rsif.2020.0393
发表时间：
2020-11
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Hébert-Dufresne L;Althouse BM;Scarpino SV;Allard A
通讯作者：
Allard A
影响因子：
33.9
作者：
Dyal, Jonathan W.;Grant, Michael P.;Honein, Margaret A.
通讯作者：
Honein, Margaret A.
DOI：
10.3760/cma.j.cn112338-20200228-00207
发表时间：
2020-04-10
影响因子：
--
作者：
Gao, W J;Li, L M
通讯作者：
Li, L M
影响因子：
56.9
作者：
Britton, Tom;Ball, Frank;Trapman, Pieter
通讯作者：
Trapman, Pieter
影响因子：
82.9
作者：
He, Xi;Lau, Eric H. Y.;Leung, Gabriel M.
通讯作者：
Leung, Gabriel M.