Viral tests including polymerase chain reaction (PCR) tests are recommended to diagnose COVID-19 infection during the acute phase of infection. A test should have high sensitivity; however, the sensitivity of the PCR test is highly influenced by viral load, which changes over time. Because it is difficult to collect data before the onset of symptoms, the current literature on the sensitivity of the PCR test before symptom onset is limited. In this study, we used a viral dynamics model to track the probability of failing to detect a case of PCR testing over time, including the presymptomatic period. The model was parametrized by using longitudinal viral load data collected from 30 hospitalized patients. The probability of failing to detect a case decreased toward symptom onset, and the lowest probability was observed 2 days after symptom onset and increased afterwards. The probability on the day of symptom onset was 1.0% (95% CI: 0.5 to 1.9) and that 2 days before symptom onset was 60.2% (95% CI: 57.1 to 63.2). Our study suggests that the diagnosis of COVID-19 by PCR testing should be done carefully, especially when the test is performed before or way after symptom onset. Further study is needed of patient groups with potentially different viral dynamics, such as asymptomatic cases.

建议在感染的急性期进行包括聚合酶链反应（PCR）检测在内的病毒检测，以诊断COVID-19感染。测试应具有较高的灵敏度；但是，PCR检测的灵敏度受到病毒载量的很大影响，病毒载量随时间变化。由于很难在症状发作之前收集数据，因此有关症状发作之前PCR检测灵敏度的最新文献是有限的。在这项研究中，我们使用病毒动力学模型来追踪一段时间内（包括症状前期）未能检测到PCR检测病例的概率。通过使用从30例住院患者中收集的纵向病毒载量数据对模型进行参数化。未能发现病例的可能性随着症状发作而降低，症状发作后2天观察到的可能性最低，此后增加。症状发作当天的概率为1.0％（95％CI：0.5至1.9），症状发作前2天的概率为60.2％（95％CI：57.1至63.2）。我们的研究表明，通过PCR检测对COVID-19的诊断应谨慎进行，尤其是在症状发作之前或之后进行。需要进一步研究具有潜在不同病毒动力学的患者群体，例如无症状病例。