Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.
Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.
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DOI：
10.1016/j.cma.2020.113410
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发表时间：
2020-12-01
影响因子：
7.2
通讯作者：
Kuhl E
中科院分区：
文献类型：
--
作者：
Peirlinck M;Linka K;Sahli Costabal F;Bhattacharya J;Bendavid E;Ioannidis JPA;Kuhl E
了解COVID-19大流行的爆发动态对成功遏制和缓解策略具有重要意义。最近的研究表明，SARS-CoV-2抗体的人群流行率（无症状病例数量的代表）可能比报告的有症状病例数量的预期大一个数量级。了解无症状传播的准确流行率和传染性对于估计COVID-19的总体规模和大流行潜力至关重要。然而，在现阶段，无症状人群的影响、其规模及其暴发动态在很大程度上仍然未知。在这里，我们使用报告的症状病例数据，结合抗体血清阳性率研究、数学流行病学模型和贝叶斯框架来推断COVID-19的流行病学特征。我们的模型计算，在真实的时间，随时间变化的接触率的爆发，并项目的时间演变和可信区间的有效繁殖数量和有症状的，无症状的，和恢复的人群。我们的研究量化了COVID-19疫情动态对三个参数的敏感性：有效繁殖数、有症状和无症状人群的比例以及两组人群的感染期。对于九个不同的地点，我们的模型估计到2020年6月15日已感染并康复的人口比例为24.15% Heinsberg（95% CI：20.48%-28.14%）（北威州，德国），2.40%（95% CI：2.09%-2.76%），Ada County（ID，USA），46.19%（95% CI：45.81%-46.60%），纽约市（NY，USA），11.26%（95% CI：7.21%-16.03%），圣克拉拉县（CA，USA），3.09%（95% CI：2.27%-4.03%），丹麦，12.35%（95% CI：10.03%-15.18%），日内瓦州（瑞士），5.24%（95% CI：4.84%-5.70%），南里奥格兰德（巴西）1.53%（95% CI：0.76%-2.62%），比利时5.32%（95% CI：4.77%-5.93%）。我们的方法将圣克拉拉县的最初爆发日期追溯到2020年1月20日（95% CI：2019年12月29日-2020年2月13日）。我们的研究结果可能会显著改变我们对COVID-19大流行的理解和管理：大量无症状人群将使隔离、遏制和追踪个别病例变得具有挑战性。相反，通过提高人口意识、促进身体距离和鼓励行为改变来管理社区传播可能变得更加重要。最近的研究表明，COVID-19在大多数情况下仍然没有症状。COVID-19的无症状传播是不可见的，人们对它的了解仍然很少。我们使用贝叶斯框架内的SEIIR模型可视化无症状传播。我们说明了9个地点的有症状和无症状人群的相互作用。了解无症状传播可能会改变我们管理COVID-19的方式。
Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019–February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant. Recent studies suggest that COVID-19 remains asymptomatic in most cases. The asymptomatic transmission of COVID-19 is invisible and remains poorly understood. We visualize asymptomatic transmission using an SEIIR model within a Bayesian framework. We illustrate the interplay of symptomatic and asymptomatic populations for nine locations. Understanding asymptomatic transmission could change the way we manage of COVID-19.
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影响因子：
7.8
作者：
ENGBERT, R;DREPPER, FR
通讯作者：
DREPPER, FR
影响因子：
11.8
作者：
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DOI：
10.1073/pnas.0307506101
发表时间：
2004-04-20
影响因子：
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通讯作者：
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影响因子：
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