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
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DOI：
10.1101/2020.06.30.20143115
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发表时间：
2021-03-01
期刊：
影响因子：
3.8
通讯作者：
Mordecai, Erin A.
中科院分区：
文献类型：
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作者：
Kain, Morgan P.;Childs, Marissa L.;Mordecai, Erin A.
众所周知，疾病传播具有异质性，SARS-CoV-2 也不例外。少数个人或事件负责大部分传播的倾斜分布可能会导致爆炸性超级传播事件，从而产生快速且不稳定的流行病动态，尤其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幅降低总体传输速率。在这里，我们提出了一个随机区室 (SEIR) 流行病学模型框架，用于根据多个不完全观察到的数据流来估计传播参数，包括报告的病例、死亡和基于手机的移动性，其中使用先前对 SARS-CoV-1 和 SARS-CoV-2 的估计将个人层面的传播异质性纳入其中。我们通过估计随时间变化的传播率来参数化 COVID-19 流行病动态模型，该传播率纳入了五个流行病学不同的地区（加利福尼亚州洛杉矶县和圣克拉拉县）中随时间变化的非药物干预策略的影响。华盛顿州西雅图（金县）；佐治亚州亚特兰大（迪卡尔布县和富尔顿县）；和佛罗里达州迈阿密（迈阿密戴德县）。我们发现，2020 年 3 月中旬实行社交距离令后，所有五个地点的有效传染数 (R-E) 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 R-E 在 5 月底（洛杉矶、迈阿密和亚特兰大）或 6 月初（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，为应对疫情死灰复燃而加强的社交距离，到 8 月 14 日，所有地点的 R-E 再次降至 1 以下。接下来，我们使用拟合模型来问：截断个体传播率分布（消除个体传播率特别高的时间段，从而模型超级传播事件）如何影响疫情动态和控制？我们发现，在部分放松社交距离的同时截断传播率分布的干预措施总体上是有效的，对疫情增长的影响与2020年4月观察到的最强烈的全民社交距离相当。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疫情控制，“砍掉尾巴”以降低超级传播事件的可能性是缓解极端普遍社交距离需求的一个有希望的选择。
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 (R-E) 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 R-E 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 R-E 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.