Estimating an epidemic’s trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance—in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing—changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic’s trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.

估计流行病的轨迹对于制定针对传染病的公共卫生应对措施至关重要，但用于此类估计的病例数据因检测实践的变化而变得混乱。我们表明，在随机或基于症状的监测下观察到的病毒载量的群体分布（以从逆转录定量聚合酶链反应测试获得的循环阈值（Ct）的形式）在流行病期间发生变化。因此，即使是有限数量的随机样本的 Ct 值也可以提供对流行病轨迹的改进估计。组合来自多个此类样本的数据可以提高该估计的精度和稳健性。我们将我们的方法应用于严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 大流行期间在各种环境下进行的监测的 Ct 值，并为实时估计流行轨迹以进行疫情管理和响应提供替代方法。