论文标题
对在有偏采样条件下流行病的动态传播的信心
Confidence in the dynamic spread of epidemics under biased sampling conditions
论文作者
论文摘要
对抽样数据的解释在对疾病在流行期间的疾病传播的反应中起着至关重要的作用，例如2020年的Covid-19流行病。但是，这是一项非平凡的努力，由于现实世界病的复杂性以及限制了对诊断症状的偏见，因此对诊断的偏见有限制。为了得出准确的结论，需要彻底了解采样信心和偏见。在本手稿中，我们提供了一种随机模型，用于评估对疾病指标的信心，例如趋势检测，峰值检测和疾病扩散估计。我们的模型模拟了具有已知动力学的流行病中疾病的测试，从而使我们能够使用蒙特卡洛抽样来评估度量置信度。该模型可以提供可实现的模拟数据，这些数据可用于数据分析和预测方法的设计和校准。例如，我们使用这种方法表明，每天使用$ 10000 $偏见的样品可以识别该疾病的趋势，并且可以使用额外的$ 1000-2000 $无偏见的样本来进行疾病蔓延的估计。我们还证明，该模型可用于通过查找在动态中找到峰的精确度和回忆来评估更高级的指标。
The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection, and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under $10000$ biased samples each day, and an estimate of disease spread can be made with additional $1000-2000$ unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.
