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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19
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Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19

机译：可视化隐形：无症状传输对Covid-19爆发动态的影响

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摘要

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. (c) 2020 Elsevier B.V. All rights reserved.
机译：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抗体的人口普遍性，一种用于无症状的案例的代理，可能是报告症状病例的数量大于预期的数量级。了解无症状传输的精确普遍性和传染性对于估计Covid-19的整体维度和大流行潜力至关重要。然而，在这个阶段，无症状人口，其规模和爆发动态的影响仍然很大程度上是未知的。在这里，我们将报告的对症案例数据结合抗体Seroprevalences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。我们的模型实时计算了爆发的时变的接触率，并投影了有效再现数量和症状，无症状和恢复的人群的时间演化和可信的间隔。我们的研究量化了Covid-19至三个参数的爆发动力学的敏感性：有效的再现数，症状性和无症状的群体之间的比例，以及两组的传染期。对于九个不同的地点，我们的模型估计，海斯伯格（NRW，德国NRW），2.40％（95岁），24.15％（95％CI：20.48％-28.48％-28.14％），估计已被感染和恢复的人口的一部分。纽约市（纽约，美国），46.19％（美国纽约市），46.19％（95％CI：45.81％-46.6.6.6.60％），11.26％（95％CI：7.21％），46.19％（95％CI：45.81％-46.60％）为圣克拉拉县（美国，美国），3.09％（95％CI：2.27％-4.03％）为丹麦，12.35％（95％CI：10.03％-15.18％），为日内瓦广州（瑞士），荷兰5.24％（95％CI：4.84％-5.70％）为RIO Grande Do Sul（巴西）的1.53％（95％CI：0.76％-2.62％），5.32％（95％CI：4.77％ - 比利时5.93％）。我们的方法追溯了圣克拉拉县的初始爆发日，返回2020年1月20日（95％CI：2019年12月29日 - 2月13日，2020年）。我们的结果可以大大改变我们对Covid-19大流行的理解和管理：大的无症状人口将孤立，遏制和追踪个体案件具有挑战性的。相反，通过增加人口意识，促进身体疏散和鼓励行为变化来管理社区传输可能会变得更加相关。 （c）2020 Elsevier B.v.保留所有权利。

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