Department of Computational Medicine, University of California, Los Angeles, 90095-1766 Los Angeles, CA, USA.
Computational Social Science, Frankfurt School of Finance and Management, 60322 Frankfurt am Main, Germany.
Philos Trans A Math Phys Eng Sci. 2022 Jan 10;380(2214):20210121. doi: 10.1098/rsta.2021.0121. Epub 2021 Nov 22.
We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.
我们开发了一个用于检测人群中疾病流行率的统计模型。该模型假设测试结果为二分类，阳性或阴性，但允许样本选择存在偏差，以及存在 I 类（假阳性）和 II 类（假阴性）测试错误。我们的模型还结合了多种测试类型，并能够区分测试后的重复测试和排除。我们的定量框架允许我们将测试结果直接解释为误差和偏差的函数。通过将我们的测试模型应用于 COVID-19 测试数据和特定管辖区的实际病例数据，我们能够估计和提供在大流行中至关重要的指标的不确定性量化，例如疾病流行率和病死率比。本文是“传染病监测数据科学方法”主题特刊的一部分。
