Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.
Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.
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DOI：
10.1371/journal.pcbi.1008994
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Santillana M
中科院分区：
文献类型：
--
作者：
Lu FS;Nguyen AT;Link NB;Molina M;Davis JT;Chinazzi M;Xiong X;Vespignani A;Lipsitch M;Santillana M
有效设计和评估针对当前 COVID-19 大流行的公共卫生应对措施需要准确估计全美国 (US) 的 COVID-19 流行情况。然而，设备短缺和检测能力参差不齐，阻碍了官方报告的 COVID-19 阳性病例数的实用性。我们引入了四种补充方法，结合使用过多的流感样疾病报告、COVID-19 测试统计数据、COVID-19 死亡率报告和空间结构化流行病模型，来估计美国各州以及波多黎各和哥伦比亚特区有症状的 COVID-19 的累积发病率。我们不是依赖于可能有偏差的单一数据源或方法的估计，而是提供多个估计，每个估计都依赖于不同的假设和数据源。通过我们的四种方法，得出了一致的结论：2020 年 4 月 4 日，各州的估计病例数比官方公布的阳性检测数高出 5 至 50 倍。在全国范围内，截至 4 月 4 日，我们对有症状的 COVID-19 病例的估计范围可能为 2.3 至 480 万例，其中可能多达 760 万例，比累计确诊病例约 311,000 例高出 25 倍。将我们的方法延伸至 2020 年 5 月 16 日，我们估计累积症状发生率范围为 4.9 至 1010 万，而不是阳性检测计数 150 万。所提出的方法组合可能有助于评估美国和其他拥有类似监测系统的国家再次出现的新冠肺炎 (COVID-19) 造成的负担。准确估计美国 COVID-19 的每周发病率对于规划和研究有效的公共卫生应对措施至关重要。由于美国各地系统性检测短缺，官方的 COVID-19 阳性检测计数并不是真实发病率的可靠指标。在本研究中，我们提出了四种估计累积发生率的替代方法，这些方法利用了不同的数据源和假设。在全国范围内，截至 4 月 4 日，我们对有症状的 COVID-19 病例的估计范围可能为 2.3 至 480 万例，其中可能多达 760 万例，比累计确诊病例约 311,000 例高出 25 倍。我们强调，比较多个模型而不是依赖单一方法可以更可靠地估计 COVID-19 发病率。我们的方法可能有助于追踪 COVID-19 在美国和其他国家的死灰复燃。
Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems. Accurate estimates of the weekly incidence of COVID-19 in the United States is essential for planning and researching effective public health responses. Because of systematic testing shortages across the United States, official positive COVID-19 test counts are an unreliable indicator of true incidence. In this study, we present four alternative approaches for estimating cumulative incidence, which leverage different data sources and assumptions. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. We emphasize that comparing multiple models rather than relying on a single method gives more reliable estimates of COVID-19 incidence. Our approaches could be useful for tracking the resurgence of COVID-19 in the United States as well as in other countries.
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DOI：
10.1073/pnas.0906910106
发表时间：
2009-12-22
影响因子：
11.1
作者：
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
3.5
作者：
Eubank, S.;Eckstrand, I;Barrett, C. L.
通讯作者：
Barrett, C. L.
影响因子：
3.3
作者：
Balcan, Duygu;Goncalves, Bruno;Hu, Hao;Ramasco, Jose J.;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
56.9
作者：
Chinazzi, Matteo;Davis, Jessica T.;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
8.5
作者：
Baltrusaitis, Kristin;Vespignani, Alessandro;Santillana, Mauricio
通讯作者：
Santillana, Mauricio