Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.
Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.
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DOI：
10.1073/pnas.2103272118
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发表时间：
2021-08-03
影响因子：
11.1
通讯作者：
Raftery AE
中科院分区：
文献类型：
--
作者：
Irons NJ;Raftery AE
新型冠状病毒SARS-CoV-2已经感染了美国超过3300万人。在全国范围内，已有超过60万人死于新冠肺炎疫情，导致学校和经济部门停课。由于测试数据中的偏差，病毒传播的程度仍然不确定。我们结合多个数据来源来估计美国所有州的真实感染人数。这些数据包括来自印第安纳州和俄亥俄州的代表性随机测试调查，这些调查提供了潜在的无偏见的流行率估计。我们发现，大约60%的感染没有上报。即便如此，截至2021年3月初，美国只有约20%的地区被感染，这表明当时这个国家距离群体免疫力还很远。有多个数据来源提供了有关人口中SARS-CoV-2感染人数的信息，但所有数据都有重大缺陷，包括偏见和报告延迟。例如，确诊病例的数量在很大程度上低估了感染人数，死亡大大滞后于感染，而检测阳性率往往大大高估了流行率。代表性的随机流行率调查是唯一被认为没有偏见的来源，在时间和空间上都是稀疏的，结果可能会有很大的延迟。对人口流行率的可靠估计对于了解病毒的传播和缓解战略的有效性是必要的。我们开发了一个简单的贝叶斯框架，通过结合几个主要的可用数据来源来估计病毒的流行程度。它基于具有时变繁殖参数的离散时间易感感染移除(SIR)模型。我们的模型包括可能性部分，其中包括因病毒死亡、确诊病例和每天进行的检测次数的数据。我们的推论来自印第安纳州和俄亥俄州的随机抽样测试调查数据。我们使用这两个州的结果来校准阳性检测计数的模型，并进而估计美国每个州的感染死亡率和每天新感染的人数。我们估计了报告的COVID病例低估了真实感染人数的程度，真实感染人数很大，特别是在大流行的头几个月。我们探讨了我们的结果对群体免疫进展的影响。
The novel coronavirus SARS-CoV-2 has infected over 33 million people in the United States. Nationwide, over 600,000 have died in the COVID-19 pandemic, which has necessitated shutdowns of schools and sectors of the economy. The extent of the virus’ spread remains uncertain due to biases in test data. We combine multiple data sources to estimate the true number of infections in all US states. These data include representative random testing surveys from Indiana and Ohio, which provide potentially unbiased prevalence estimates. We find that approximately 60% of infections have gone unreported. Even so, only about 20% of the United States had been infected as of early March 2021, suggesting that the country was far from herd immunity at that point. There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available data sources. It is based on a discrete-time Susceptible–Infected–Removed (SIR) model with time-varying reproductive parameter. Our model includes likelihood components that incorporate data on deaths due to the virus, confirmed cases, and the number of tests administered on each day. We anchor our inference with data from random-sample testing surveys in Indiana and Ohio. We use the results from these two states to calibrate the model on positive test counts and proceed to estimate the infection fatality rate and the number of new infections on each day in each state in the United States. We estimate the extent to which reported COVID cases have underestimated true infection counts, which was large, especially in the first months of the pandemic. We explore the implications of our results for progress toward herd immunity.
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DOI：
10.1016/j.amjmed.2020.09.024
发表时间：
2021-04
期刊：
The American journal of medicine
影响因子：
--
作者：
Mahajan S;Srinivasan R;Redlich CA;Huston SK;Anastasio KM;Cashman L;Massey DS;Dugan A;Witters D;Marlar J;Li SX;Lin Z;Hodge D;Chattopadhyay M;Adams MD;Lee C;Rao LV;Stewart C;Kuppusamy K;Ko AI;Krumholz HM
通讯作者：
Krumholz HM
影响因子：
11.1
作者：
Lu, Jing;Peng, Jinju;Ke, Changwen
通讯作者：
Ke, Changwen
影响因子：
11.8
作者：
Bullard, Jared;Dust, Kerry;Poliquin, Guillaume
通讯作者：
Poliquin, Guillaume
DOI：
10.1016/j.ijid.2020.09.1464
发表时间：
2020-12
期刊：
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
影响因子：
--
作者：
Meyerowitz-Katz G;Merone L
通讯作者：
Merone L
影响因子：
82.9
作者：
He, Xi;Lau, Eric H. Y.;Leung, Gabriel M.
通讯作者：
Leung, Gabriel M.