A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors
A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors
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DOI：
10.1101/2021.05.22.21257643
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发表时间：
2022-01-10
期刊：
影响因子：
5
通讯作者：
Chou, Tom
中科院分区：
文献类型：
--
作者：
Boettcher, Lucas;D'Orsogna, Maria R.;Chou, Tom
我们开发了一个统计模型来测试人群中的疾病流行程度。该模型假设一个二元测试结果，阳性或阴性，但允许样本选择偏差和类型I（假阳性）和类型II（假阴性）测试错误。我们的模型还包含多种测试类型，并且能够区分测试后的重新测试和排除。我们的定量框架允许我们直接将测试结果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测试数据和来自特定司法管辖区的实际病例数据，我们能够估计并提供在大流行中至关重要的指数的不确定性量化，例如患病率和致死率。本文是主题“传染病监测的数据科学方法”的一部分。
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'.