Pandemics: Insurance and Social Protection
Pandemics: Insurance and Social Protection
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流行病：保险和社会保障
DOI：
10.1007/978-3-030-78334-1_11
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Aldridge M
中科院分区：
文献类型：
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作者：
Aldridge M
在检测COVID-19等疾病时，标准方法是个体检测：我们从每个人身上采集样本，并分别对这些样本进行检测。另一种方法是混合测试（或“组测试”），其中样品在不同的池中混合在一起，并对这些混合样品进行测试。当疾病的流行率较低，测试的准确性相当高时，合并测试策略可能比单独测试更有效。在本章中，我们将讨论合并测试的数学方法及其在大流行期间的应用，特别是在COVID-19大流行期间。我们分析了一些一个和两个阶段的池化策略下完美和不完美的测试，并考虑在实际应用中的问题，这样的协议。
When testing for a disease such as COVID-19, the standard method is individual testing: we take a sample from each individual and test these samples separately. An alternative is pooled testing (or ‘group testing’), where samples are mixed together in different pools, and those pooled samples are tested. When the prevalence of the disease is low and the accuracy of the test is fairly high, pooled testing strategies can be more efficient than individual testing. In this chapter, we discuss the mathematics of pooled testing and its uses during pandemics, in particular the COVID-19 pandemic. We analyse some one-and two-stage pooling strategies under perfect and imperfect tests, and consider the practical issues in the application of such protocols.