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.

中文翻译：

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可视化无形之物：无症状传播对 COVID-19 疫情动态的影响

了解 COVID-19 大流行的爆发动态对于成功的遏制和缓解策略具有重要意义。最近的研究表明，SARS-CoV-2 抗体的人群患病率（代表无症状病例数量）可能比根据报告的有症状病例数量预期高出一个数量级。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、其规模及其爆发动态仍然很大程度上未知。在这里，我们使用报告的症状病例数据结合抗体血清流行率研究、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。我们的模型实时计算疫情随时间变化的接触率，并预测有效繁殖数以及有症状、无症状和康复人群的时间演变和可信区间。我们的研究将 COVID-19 爆发动态的敏感性量化为三个参数：有效繁殖数、有症状人群与无症状人群的比例以及两组的感染期。对于九个不同的地点，我们的模型估计到 2020 年 6 月 15 日，海因斯贝格（德国北威州）已被感染并康复的人口比例为 24.15%（95% CI：20.48%-28.14%），2.40%（95艾达县（美国爱达荷州）% CI：2.09%-2.76%），纽约市（美国纽约）46.19%（95% CI：45.81%-46.60%），11.26%（95% CI：7.21%）圣克拉拉县（美国加利福尼亚州）为 -16.03%），丹麦为 3.09%（95% CI：2.27%-4.03%），日内瓦州（瑞士）为 12.35%（95% CI：10.03%-15.18%），荷兰为 5.24%（95% CI：4.84%-5.70%），南里奥格兰德州（巴西）为 1.53%（95% CI：0.76%-2.62%），5.32%（95% CI：4.77%- 5.93%）比利时。我们的方法将圣克拉拉县的首次爆发日期追溯到2020年1月20日（95% CI：2019年12月29日至2020年2月13日）。我们的结果可能会显着改变我们对 COVID-19 大流行的理解和管理：大量无症状人群将使个体病例的隔离、遏制和追踪变得具有挑战性。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会变得更加重要。