Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States
Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States
复制标题
DOI：
10.1126/scitranslmed.abc1126
复制
发表时间：
2020-07-29
影响因子：
17.1
通讯作者：
Washburne, Alex D.
中科院分区：
文献类型：
--
作者：
Silverman, Justin D.;Hupert, Nathaniel;Washburne, Alex D.
迄今为止，严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）感染的检测在很大程度上依赖于逆转录聚合酶链反应检测。然而，有限的测试可用性，高假阴性率，以及无症状或亚临床感染的存在，导致SARS-CoV-2的真实患病率被低估。在这里，我们展示了如何流感样疾病（ILI）门诊监测数据可以用来估计SARS-CoV-2的流行。我们发现2020年3月非流感ILI激增至高于季节平均水平，并显示这一激增与各州2019年冠状病毒病（COVID-19）病例数相关。如果美国有三分之一的SARS-CoV-2感染者寻求治疗，那么在2020年3月8日至28日的3周期间，ILI的激增将相当于美国新增870多万例SARS-CoV-2感染。将ILI的过量计数与美国社区传播的开始日期相结合，我们还表明，美国早期的流行病不太可能比每4天增加一倍。总之，这些结果为美国的COVID-19疫情提供了一个概念模型，其特征是在美国迅速传播，超过80%的感染者未被发现。我们强调用血清阳性率数据检验这些发现的重要性，并讨论了使用症状监测早期发现和了解新出现的传染病的更广泛的潜力。
Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections to date has relied heavily on reverse transcription polymerase chain reaction testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or subclinical infections have resulted in an undercounting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with coronavirus disease 2019 (COVID-19) case counts across states. If one-third of patients infected with SARS-CoV-2 in the United States sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the United States during the 3-week period from 8 to 28 March 2020. Combining excess ILI counts with the date of onset of community transmission in the United States, we also show that the early epidemic in the United States was unlikely to have been doubling slower than every 4 days. Together, these results suggest a conceptual model for the COVID-19 epidemic in the United States characterized by rapid spread across the United States with more than 80% infected individuals remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.