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Abstract：
Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.
We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.
Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.
The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.
We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.
Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.
The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.
摘要：
尽管广泛使用，SARS-CoV-2感染诊断试验的准确性尚不清楚.我们工作的目的是更好地量化识别COVID-19真实病例的错误分类错误，并使用来自艾伯塔省的公开监测数据研究这些错误对流行病曲线的影响。加拿大和费城,美国。
我们检查了SARS-CoV-2病毒感染实验室测试的时间序列数据，COVID-19的因果关系，试图探索，使用贝叶斯方法，诊断试验的敏感性和特异性。
我们的分析显示，这些数据与接近完美的特异性一致，但是要获得有关敏感性的信息是具有挑战性的。在敏感性改善和降低的假设下，我们将这些见解应用于流行病曲线的不确定性/偏差分析。如果灵敏度从60%提高到95%，调整后的流行曲线可能落在观察计数的95%置信区间内.然而,流行病曲线的形状和峰值的偏差可以很明显，如果灵敏度在60-70%的范围内下降或仍然很差。在极端情况下,数以百计的未确诊病例,即使在测试中，是可能的,如果这些病例不自我隔离，可能会导致进一步的不受控制的传染。
更好地了解由于测试错误而导致的COVID-19流行曲线偏差的最佳方法是根据经验评估临床环境中诊断的错误分类，并将这些知识应用于调整流行曲线。
我们检查了SARS-CoV-2病毒感染实验室测试的时间序列数据，COVID-19的因果关系，试图探索，使用贝叶斯方法，诊断试验的敏感性和特异性。
我们的分析显示，这些数据与接近完美的特异性一致，但是要获得有关敏感性的信息是具有挑战性的。在敏感性改善和降低的假设下，我们将这些见解应用于流行病曲线的不确定性/偏差分析。如果灵敏度从60%提高到95%，调整后的流行曲线可能落在观察计数的95%置信区间内.然而,流行病曲线的形状和峰值的偏差可以很明显，如果灵敏度在60-70%的范围内下降或仍然很差。在极端情况下,数以百计的未确诊病例,即使在测试中，是可能的,如果这些病例不自我隔离，可能会导致进一步的不受控制的传染。
更好地了解由于测试错误而导致的COVID-19流行曲线偏差的最佳方法是根据经验评估临床环境中诊断的错误分类，并将这些知识应用于调整流行曲线。
